Change Log: - Radiological Society of North America



QIBA Profile:Computed Tomography: Lung DensitometryTable of Contents TOC \o "1-3" \h \z \u Change Log: PAGEREF _Toc507843401 \h 4Open Issues: PAGEREF _Toc507843402 \h 5Closed Issues: PAGEREF _Toc507843403 \h 71. Executive Summary PAGEREF _Toc507843404 \h 102. Clinical Context and Claims PAGEREF _Toc507843405 \h 113. Profile Activities PAGEREF _Toc507843406 \h 143.1. Product Qualification PAGEREF _Toc507843407 \h 153.1.1 Discussion PAGEREF _Toc507843408 \h 153.1.2 Specification PAGEREF _Toc507843409 \h 163.2. Staff Qualification PAGEREF _Toc507843410 \h 17 HYPERLINK \l "_Toc507843411" 3.2.1 Discussion PAGEREF _Toc507843411 \h 18 HYPERLINK \l "_Toc507843412" 3.2.2 Specification PAGEREF _Toc507843412 \h 183.3. Periodic QA PAGEREF _Toc507843413 \h 183.3.1 Discussion PAGEREF _Toc507843414 \h 183.3.2 Specification PAGEREF _Toc507843415 \h 183.4. Protocol Design PAGEREF _Toc507843416 \h 19 HYPERLINK \l "_Toc507843417" 3.4.1 Discussion PAGEREF _Toc507843417 \h 19 HYPERLINK \l "_Toc507843418" 3.4.2 Specification PAGEREF _Toc507843418 \h 213.5. Subject Handling PAGEREF _Toc507843419 \h 223.5.1 Discussion PAGEREF _Toc507843420 \h 223.5.2 Specification PAGEREF _Toc507843421 \h 243.6. Image Data Acquisition PAGEREF _Toc507843422 \h 253.6.1 Discussion PAGEREF _Toc507843423 \h 253.6.2 Specification PAGEREF _Toc507843424 \h 253.7. Image Data Reconstruction PAGEREF _Toc507843425 \h 263.7.1 Discussion PAGEREF _Toc507843426 \h 263.7.2 Specification PAGEREF _Toc507843427 \h 273.8. Image QA PAGEREF _Toc507843428 \h 273.8.1 Discussion PAGEREF _Toc507843429 \h 273.8.2 Specification PAGEREF _Toc507843430 \h 273.9. Image Distribution PAGEREF _Toc507843431 \h 283.10. Image Analysis PAGEREF _Toc507843432 \h 283.10.1 Discussion PAGEREF _Toc507843433 \h 283.10.2 Specification PAGEREF _Toc507843434 \h 293.11. Image Interpretation PAGEREF _Toc507843435 \h 293.11.1 Discussion PAGEREF _Toc507843436 \h 293.11.2 Specification PAGEREF _Toc507843437 \h 304. Assessment Procedures PAGEREF _Toc507843438 \h 304.1.1 Assessment Procedure: HU Bias PAGEREF _Toc507843439 \h 314.1.3 Assessment Procedure: In-Plane Spatial Resolution and Edge Enhancement PAGEREF _Toc507843440 \h 334.1.3 Assessment Procedure: Through-Plane (Z-axis) Spatial Resolution PAGEREF _Toc507843441 \h 334.2. Assessment Procedure: Repeatability of Analysis Software PAGEREF _Toc507843442 \h 335. Conformance PAGEREF _Toc507843443 \h 34References PAGEREF _Toc507843444 \h 34Appendices PAGEREF _Toc507843445 \h 38Appendix A: Acknowledgements and Attributions PAGEREF _Toc507843446 \h 38Appendix B: Background Information PAGEREF _Toc507843447 \h 38Appendix C: Conventions and Definitions PAGEREF _Toc507843448 \h 55Appendix D: Model-specific Instructions and Parameters PAGEREF _Toc507843449 \h 57This Appendix provides analysis software parameters that are expected to be compatible with the profile requirements. PAGEREF _Toc507843450 \h 58Change Log:This table is a best-effort of the authors to summarize significant changes to the Profile.DateSections AffectedSummary of Change2016.06.21AllFirst Draft2016.07.20Up to Section 3Per call discussion, modified specification for lung inflation volume. Completed changes through Section 2 with draft to be reviewed for internal comment.2016.07.24Section 3Assigned for discussion at next call (tentatively 2016.08.03)2016.08.31AllCleanup of current discussions, additions to Section 3.2016.09.26Discussed Thru Section 3.2Took comments during call on organization, phantom specifications and statistics. 2016.11.16Updated Section 4.0, Assessment ProceduresInserted text documenting detailed harmonization calculations provided by Dr. Chen-Mayer2016.11.16Addressed Call Comments Through Section 3Final approval from group on call.2017.02.01Updates to Calibration Sections for Scanner Qualification (3.1)Stephen Humphries2017.03.29Revisions to Clinical Interpretation Sections and to finalize profileDavid Lynch and Sean Fain2017.08.13Added example of clinical context of claimsAEC moved to closed issue.3.3.2 and 3.6.2 specifications made consistent with textReferences updated.Appendix D addedModifications to section 3.8 (software analysis)Created Section 4.3 and place holder for Appendix E.Sean Fain - Modifications in response to face to face meeting suggestions and 6/28/17 call. 2017.08.301. Moved AEC back to “Open Issue”2. Created a Clean Version for Distribution2017.11.19Addressed concerns from external/internal review (Kevin O’Donnell, Nancy Obuchowski, and Phil Judy).2018.03.04Final Edits from Committee DiscussionsS. Fain and Jared Jered Sieren: Draft for internal distribution to committee in anticipation of distribution for public comment2018.07.30Additional Edits incorporated from committee discussion per Dr. LynchModified “shall’s” to recommended best methods. Changed radiologist actor to technologist for routine specifications.2018.11.26Revised draft distributed for RSNA discussionMainly clean up of edits to guide discussion and incorporation of software comparison guidelines.2018.12.15Revised draft SBFIncorporated edits and software comparison updates from Miranda Kirby.2019.02.25Revised draft SBFIncorporated suggestions from committee to be more specific about the use of the COPDGene phantom throughout qualification sections. Addressed potential bias in HU near -1000 due to truncation by adding footnotes to claims and specification tables where appropriate. Fixed typos in sections 4.1.1.; Updated section 4.2 to more clearly describe procedure for qualifying a new vendor software analysis tool.Open Issues:The following issues are provided here to capture associated discussion, to focus the attention of reviewers on topics needing feedback, and to track them so they are ultimately resolved. In particular, comments on these issues are highly encouraged during the Public Comment stage.1. “Iterative Reconstruction”Q. What is the effect of iterative reconstruction (IR) methods on RA-950 HU and Perc 15Perc15 measures of lung parenchymal density?A. The resolution of this question is actively under investigation as part of a development/ground work project. The use of IR would be desirable to reduce CT dose to research subjects and patients undergoing quantitative CT of the lungs. Several published works have emerged, one in Medical Physics from this committee, demonstrating that IR methods reduce noise and have non-linear effects on texture and other low contrast structures. Further complicating this issue, vendors use different statistical and model-based IR methods in their commercial software that may affect image noise differently and would thus need to be harmonized across vendors. Additionally, IR methods are likely to continue to evolve, and continued ongoing assessment will be needed. We anticipate that after further study, recommendations for integration of IR methods into the Profile can be added to later versions.2. “Harmonization”Q. What is the best reference standard for harmonizing systematic differences in quantitative CT number (Hounsfield Unit – HU) across scanner make and model?A. Two rounds of scans conducted with engineers from 4 major vendors (Siemens, GE, Toshiba, and Philips) using the QIBA-SRM phantom to establish bias and precision of HU measure have been completed. A method to empirically correct to a common reference scanner has been shown to reduce bias and improve precision of qCT measures in this phantom. A model-based correction method based on the composition (best-knowledge) and the certified physical densities of the constituent materials of the QIBA-SRM phantom has also shown promise as an absolute correction (standardization) method, and a manuscript is under review by Medical Physics. The standardization method is being actively pursued and tested in the Round 2 analysis, with data acquisition completed and analysis pending. The harmonization method is provisionally included in the current profile as a recommended assessment procedure (Section 4.2) with the goal of addressing a cross-sectional claim in future versions of the profile.3. “Airway measures”Q. Does this profile meet any standards for airway morphology (e.g. wall thickness and lumen area) measurement?A. This profile meets claims for parenchymal density analysis only, but spatial resolution specifications under acquisition and reconstruction are also consistent with current methods in the literature for measurement of airway morphology assuming commercially available software analysis methods. A claim for airway measures is left for future versions of the Profile. 4. “Regional measures”Q. Should the measurement methods specify regional measures (e.g. upper, middle, lower thirds) or lobar segmentation and density measurement methods used to guide lung volume reduction interventions?A. There is currently inadequate evidence to evaluate this issue, but there is little doubt that lobar segmentation is an emerging application for targeted lung volume reduction (by valve placement or surgically). While lobar specific density analysis is not part of the current profile, we expect to address this directly in future versions. However, it should be noted that there is no reason to think that the proposed protocols, including acquisition and reconstruction would not still serve this application equally well with the same quality assurance processes, not including software analysis. The main challenge preventing us from including this application in the claims is that some software analysis and measurement steps would need to be performed at the lobar level, raising new challenges for accuracy and precision of segmentation, especially using automated techniques. More effort to characterize consistency and consequences of errors in lobar segmentation for lung volume reduction applications are needed to define claims for this application. 5. “Automated Exposure Control (AEC)”Q. What is the effect of automated exposure control (AEC) on RA-950 HU and Perc 15Perc15 across scanner make and model?A. The use of AEC is desirable as a method that reduces dose and makes noise behavior more consistent throughout the image by matching tube current to achieve similar photon counts across varying structural attenuation. However, different vendors match performance to the selected AEC parameter to emphasize different features in the image and proprietary models are used to predict tube current modulation based on initial scout scans. Ground work performed by the Lung Density Biomarker Committee has led to harmonized protocols that match CT dose for AEC parameter selection across scanner makes and models (see protocol examples in Appendix D). More study is needed to resolve this issue in general, but the committee considers that AEC is sufficiently mature and resolved by empirically matching settings across scanner makes and models for an average sized patient (i.e. 75 kg) for inclusion in study protocols. In the present version, the amount of radiation higher or lower than a target of 3 mGy is based on patient size and shape according to each manufacturer’s AEC attenuation model. CT radiation dose in the chest is expected to vary by ± 18% for subject weight (see Huda et al., Med Phys. 2010 Feb;37(2):842-7). between 50-100 kg, which is considered sufficiently small to be within the range of equivalent performance for the current claims as stated. Recommendations will be updated in later versions of this profile to recommend general solutions to support cross-sectional claims.Closed Issues:The following issues have been considered closed by the biomarker committee. They are provided here to forestall discussion of issues that have already been raised and resolved, and to provide a record of the rationale behind the resolution.1. “Elastic Clause”Q. Is this template open to further revisions?A. Yes.This is an iterative process by nature.Submit issues and new suggestions/ideas to the QIBA Process Cmte.2. “Biomarkers”Q. Choice of key biomarkers of lung density resolved?A. Yes, RA-950 HU and Perc 15Perc15 are the most established measures of emphysema, as both are validated against tissue histology. Perc 15Perc15, or Perc 15Perc15 adjusted for lung volume, is used most ubiquitously in clinical research trials.3. “Regional measures”Q. Whole lung or regional (e.g. lobar) measures of density recommended?A. Whole lung at present. Regional measures may be introduced in later versions.4. “Airways measures”Q. Will airway measures be included?A. No.5. “Air trapping measures”Q. Will density measures of air trapping be included?A. No.6. “Lung Volume Adjustment”Q. Will lung volume adjustment of RA-950 HU and Perc 15Perc15 be included in the Profile?A. Yes, but no specific method will be recommended in this version of the Profile as this remains a developing area of research.7. “Breath-hold Consistency”Q. There is a concern that a subject with 2 weak efforts, but less than 10% difference in lung inflation would still be accepted by this profile. Is a subject with this type of effort adequately quantifiable by this profile? A. Yes. Based on review of Dr. Park’s study in the meta-analysis, the range of fractional change of volume, V2/V1, is [0.90, 1.11], i.e. [-10%, +11%]. This was the study with the longest time interval between baseline and follow-up scans. This case is well addressed by the current Profile in “Section 3.3 Subject Handling” where it is explicitly described that the breath-hold coaching required conforms to a lung inflation standard that would meet the claims. Because we are assessing longitudinal change, it is less important (but still desirable) that the subject be within 90% of vital capacity. Published works do not in practice require spirometric gating (see Gierada et al., Radiology 220(2):448-454.), nor was spirometric gating of breath-hold used in the studies included in the meta-analysis.8. “Cross-Sectional Claim”Q. Will there be a cross-sectional claim?A. No, there is not sufficient data at the present time to support a cross-sectional claim. Both current claims are longitudinal, reporting change in emphysema extent. Current groundwork testing a harmonization method across scanner make and model, if successful, will make a cross-sectional claim feasible in future versions of the profile.9. “Specification of matrix size”Q. Should the acquisition parameters include specification of matrix size or display FOV? A. This is specified if the in-plane spatial resolution is met.1. Executive SummaryThe goal of a QIBA Profile is to achieve a repeatable and useful level of performance for measures of lung density from quantitative CT using the RA-950 HU and Perc 15Perc15 biomarkers of emphysema. Please see Appendix B for more detailed information on the calculation of and rationale for RA-950 HU and Perc 15Perc15 as the biomarkers of choice.The Claim (Section 2) describes the performance in terms of bias and precision of RA-950 HU and Perc 15Perc15 for detecting change in lung density.The Activities (Section 3) describe how to generate RA-950 HU and Perc 15Perc15 for longitudinal studies of the change in lung density. Requirements are placed on the Actors that participate in those activities as necessary to achieve the Claim in Section 2. Assessment Procedures (Section 4) for evaluating specific requirements are defined as needed. This QIBA Profile (CT: Lung Densitometry) addresses RA-950 HU and Perc 15Perc15 for longitudinal studies which are often used as biomarkers of emphysema progression in chronic obstructive pulmonary disease (COPD) or as a response to cessation of smoking and possible future treatment approaches. It places requirements on Acquisition Devices, Physicists, Technologists, Clinicians, Statisticians, Reconstruction Software and Image Analysis Software involved in Scanner Qualification, Staff Qualification, Periodic Quality Assurance, Subject Handling, Protocol Design, Image Data Acquisition, Image Data Reconstruction, Image QA, Image Distribution and Image Analysis. The requirements are focused on achieving negligible bias and avoiding unnecessary variability of the RA-950 HU and Perc 15Perc15 measurements by compensating for variations in CT number due to inconsistency of lung inflation volume and calibration of the CT scanner, and vendor-specific bias due to CT scanner make and model. To meet the claims, scanner calibration is performed using a well characterized imaging phantom ideally containing lung equivalent density foams as described in Section 4.1.The clinical performance targets are to achieve bias and repeatability such that a change in RA-950 HU of ≥ 3.7% of the normalized lung volume, or a change in Perc 15Perc15 of ≥ 11 HU can be accepted as indicative of a true change (with 95% confidence).This document is intended to help clinicians basing decisions on these biomarkers, imaging staff generating these biomarkers, vendor staff developing related products, purchasers of such products and investigators designing trials with imaging endpoints.Note that this document only states requirements to achieve the claim, not “requirements for standard of care.” Conformance to this Profile is less important than providing appropriate patient care.QIBA Profiles addressing other imaging biomarkers using CT, MRI, PET and Ultrasound can be found at qibawiki..2. Clinical Context and Claims40640358775Figure 1: Example Bland-Altman plot for perc15 without VA; the LOA (or RC when there’s no bias) are the thresholds required for detection of a true increase in the extent of emphysema. The first step is to assess whether a measurement meets the threshold for a true change (red crosses above the upper LOA would be considered subjects showing true change). The magnitude of the change can be expressed as a 95% CI: ?±18HU. 00Figure 1: Example Bland-Altman plot for perc15 without VA; the LOA (or RC when there’s no bias) are the thresholds required for detection of a true increase in the extent of emphysema. The first step is to assess whether a measurement meets the threshold for a true change (red crosses above the upper LOA would be considered subjects showing true change). The magnitude of the change can be expressed as a 95% CI: ?±18HU. Clinical Context The clinical context for this profile includes studies of quantitative longitudinal change in lung parenchymal density using image processing of CT scans acquired at different time points to quantify progression of emphysema in COPD. These studies specifically evaluate increase or decrease of lung relative area falling below a threshold of -950 HU (RA-950 HU) or the HU corresponding to a threshold at 15% of the lung relative area (Perc 15Perc15).Conformance to the requirements of this Profile by all relevant staff and equipment supports the following claims:Claim 1: Without lung Volume Adjustment (VA), an increase in RA-950* of at least 3.7%, or a decrease in Perc 15Perc15 of at least 18 HU indicates an increase in the extent of emphysema, with 95% confidence.Claim 2: With lung VA, a decrease in Perc15 of at least 11 HU, is required for detection of an increase in the extent of emphysema, with 95% confidence.*Note that in some CT scanners truncation at -1000 HU biases RA-950 values that are near zero; Such cases where low lung density lung values are completely absent, or nearly so, the HU values are not expected to be normally distributed, and the stated 95% CI no longer applies. DiscussionThe confidence intervals defined for the claims, e.g. -18 and +18 HU in Claim 1 for the Perc15 measure, define boundaries that can be thought of as “error bars” or “noise” around the measurement of lung density. If one measures change within this range, one cannot be certain that there has really been a change. However, if lung density changes beyond these limits, one can be 95% confident there has been a true change in lung density, and the observed difference is not just measurement variability. Examples of scenarios for measures that are considered within the variability expected, and thus not a true change, vs two measures that exceed the threshold for a true change are illustrated in Figure 1.Note that this does not address the biological significance of the change, just the likelihood that the measured change is real. Once a real change has been identified, the magnitude of the change can be expressed as a 95% CI. For a protocol without VA and given a measured change of ?x HU in Perc15, the true change is expected to lie in the interval [?x -18 HU, ?x +18 HU] with 95% confidence; and for a measured change of ?y% in RA-950, the true change is expected to lie in the interval [?y -3.7%, ?y +3.7%] with 95% confidence. For a protocol with VA, repeatability is improved such that given a measurement of ?x HU in Perc 15Perc15, the true change is expected to lie in the interval [?x -11 HU, ?x +11 HU] with 95% confidence. More detail on how these limits are calculated is described below and provided in Appendix B. However, it bears emphasis that VA should not be thought of as an alternative to breath-hold coaching and control. Consistent breath-hold coaching followed by simple visual inspection (e.g. by watching the subject’s chest wall through the scan room window or by camera) to confirm chest inflation is required to meet the claims. VA is a method to further improve repeatability beyond what can be achieved with prospective breath-hold coaching and control. These claims are based on estimates of the repeatability coefficient (RC) of the RA-950 HU and the Perc 15Perc15 measured from the histogram of both lungs after segmentation of the thoracic cavity and removal of blood vessels and airways as described in more detail in Section 3.8. The repeatability coefficient (RC) is defined as 1.96 2 wSD, where wSD is the within-subject standard deviation. Specific equations used to calculate the expected range for RA-950 HU and Perc 15Perc15 are defined in Appendix C. The claim assumes that there is negligible proportional bias in the measurements (i.e. bias < 5% of the measurement), and is supported by a meta-analysis (Appendix B) of studies conducted at the same site using the same scan protocol and CT scanner make and model. Further adjustment to remove bias is required when scanning subjects longitudinally on different CT scanner makes and models. Future versions of the Profile that seek to harmonize CT number or HU across different scanner makes and models may address this limitation, but this remains an open issue. Volume adjustment (VA) refers to techniques to correct for differences in lung inflation volume between time points. The literature has noted that differences in lung inflation volume are present in longitudinal studies and thus repeatability is improved using some type of VA as discussed in more detail below and in Appendix B. There are separate claims for without VA and with VA to reflect the narrower 95% confidence interval with VA. For RA-950, only 2 repeatability studies were available, which was insufficient to support a meta-analysis to inform the impact of VA on the claim for the RA-950 metric. For the studies supporting the stated claims, the method of VA varied. Because more advanced techniques for VA continue to emerge, this document does not intend to suggest any particular model or method for VA. Appendix B provides a description of VA methods that fit the selection criteria. That said, achieving consistent lung inflation volume through consistent breath-hold coaching and communication as described in Section 3.3.1 is required (and one of the most underappreciated procedures in the workflow) to achieve the claims PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5OZXdlbGw8L0F1dGhvcj48WWVhcj4yMDEzPC9ZZWFyPjxS

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ADDIN EN.CITE.DATA 1,2. To further guide the various stakeholders interested in quantitative lung density measures using CT, we include protocols in Appendix D that meet or exceed the stated claims. Clinical interpretation with respect to the magnitude of true change: Measurement of whole lung parenchymal lung density with CT has been used for several decades in clinical research as a marker of emphysema. For repeat CT examinations that are performed primarily for emphysema quantification, a change in RA-950 HU of ≥ 3.7% of the normalized lung volume, or a change in Perc 15Perc15 of ≥ 11 HU can be accepted as indicative of a true change in the extent of emphysema, with 95% confidence. Both of these measures reflect specific thresholds of the histogram of lung densities in HU and imply loss of lung tissue based on a combination of comparisons to microscopic histology and associations with known measures of whole lung function. The committee recognizes that these limits of variability are substantially greater than the average change in lung density identified in individual subjects with emphysema. In untreated subjects with alpha-1 anti-trypsin deficiency, the average annual decline in 15th percentile lung density (adjusted for lung volumes) is about 2.2 g/L per year (corresponding to 2.2 HU) PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5EaXJrc2VuPC9BdXRob3I+PFllYXI+MjAwOTwvWWVhcj48

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ADDIN EN.CITE.DATA 3,4. In cigarette smokers with COPD, the average annual decline in lung density is about 1.1 g/L, or 1.1 HU PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Db3hzb248L0F1dGhvcj48WWVhcj4yMDEzPC9ZZWFyPjxS

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bmROb3RlPn==

ADDIN EN.CITE PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Db3hzb248L0F1dGhvcj48WWVhcj4yMDEzPC9ZZWFyPjxS

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ADDIN EN.CITE.DATA 5. Given this discrepancy, the chief value of measuring change in lung attenuation will be in research cohorts in clinical trials; changes measured in individual subjects are unlikely to exceed the limits of variability. It is hoped that in the future, with greater adherence to this profile, the limits of variability can be narrowed to be more compatible with the changes expected in individual subjects. These longitudinal claims have been informed by an extensive review of the literature and expert consensus, but have not yet been substantiated by studies that strictly conform to the specifications given here. The expectation is that during field test, data on the actual field performance will be collected and any appropriate changes made to the claim or the details of the Profile. At that point, this caveat may be removed or re-stated.The performance values in the reported claims reflect the likely impact of variations permitted by this Profile since the meta-analysis was based on studies that incorporated variable methods of CT reconstruction, image analysis, and volume adjustment. The Profile thus allows for the possibility of using variable approaches to attaining lung inflation volume, CT scanner protocol and analysis tools. However, in its current form the Profile does not permit different compliant actors (specifically, acquisition device and image analysis software) be used for both exams of a patient. Again, future versions of the Profile that seek to harmonize CT number or HU across different scanner makes and models could potentially relax this requirement, but this remains an open issue.3. Profile ActivitiesThe Profile is documented in terms of “Actors” performing “Activities”. Equipment, software, staff or sites may claim conformance to this Profile as one or more of the “Actors” in the following table. Conformant Actors shall support the listed Activities by conforming to all requirements in the referenced Section. Table 1: Actors and Required ActivitiesActorActivitySectionAcquisition DeviceScanner Qualification3.1.Periodic QA3.3.Subject Handling3.5.Image Data Acquisition3.6.Physicist/TechnologistScanner Qualification3.1.Staff Qualification3.2Image QA3.8.TechnologistSubject Handling3.5.Image QA3.8.Image Data Acquisition3.6.Image Data Reconstruction3.7.Image Distribution3.9.Clinician Subject Handling3.5.Image QA3.8.Image Interpretation3.11.Reconstruction SoftwareImage Data Reconstruction3.7.Image Analysis Software and StatisticianImage Analysis3.10.Clinician and StatisticianDisease Progression3.11The requirements in this Profile do not codify a Standard of Care; they only provide guidance intended to achieve the stated Claim. Failing to conform to a “shall” in this Profile is a protocol deviation. Although deviations invalidate the Profile Claim, such deviations may be reasonable and unavoidable and the radiologist or supervising physician is expected to do so when required by the best interest of the patient or research subject. How users of this profile decide to handle deviations for analysis purposes is entirely up to them. The sequencing of the Activities specified in this Profile are shown in Figure 2. 0289560Figure 2: Computed Tomography: Lung Densitometry – Work flow0Figure 2: Computed Tomography: Lung Densitometry – Work flow3.1. Scanner Qualification This activity describes performance assessment, calibration or standardization, and validations of equipment that are necessary to reliably meet the Profile Claim. 3.1.1 DiscussionThese specifications are defined based on groundwork projects from vendor round 1 and 2 studies using the COPDGene Phantom (See “Assessment Procedures”, Figure 3) and the QIBA-SRM phantomsPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5DaGVuLU1heWVyPC9BdXRob3I+PFllYXI+MjAxNzwvWWVh

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ADDIN EN.CITE.DATA 6.Differences in scanner beam characteristics and calibrations by manufacturer and model are likely sources of systematic variation. At present the profile requires CT baseline and follow-up scans be acquired using the same scanner make and model to meet the claims. A method to empirically correct to a common reference scanner for multi-center studies has been shown to reduce bias and improve precision of quantitative CT measures in phantom testing PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5DaGVuLU1heWVyPC9BdXRob3I+PFllYXI+MjAxNzwvWWVh

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ADDIN EN.CITE.DATA 6, although this is not strictly necessary to meet the current claims. A model-based correction method based on the composition, and the certified physical densities of the constituent materials, of the QIBA-SRM phantomPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5DaGVuLU1heWVyPC9BdXRob3I+PFllYXI+MjAxNzwvWWVh

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ADDIN EN.CITE.DATA 6 has also shown promise as an absolute correction method (i.e. standardization). The standardization scheme to be used across scanner platforms is currently being devised as part of active groundwork and remains an “open issue” for this version of the Profile. The goal of that work is to eliminate scanner dependent parameters and compare the results of the true material properties such as electron density across baseline and follow-up scans that can occur potentially with different scanner makes and models. Initial qualification of a scanner involves verification that the equipment complies with specifications described in Table 3.1.2. Subsequent qualification of a scanner for evaluation of longitudinal change in lung density requires calibration of Hounsfield Unit (HU) values to improve precision and reduce bias. Modern scanners can achieve sub-HU standard deviations for intra-scanner repeat scans in the lung density region. It is desirable to confine the variations from different measurement systems to within 1 HU using an adequate test object. Because of the multitude of software programs used by different commercial, open-source, and academic research labs, an evaluation is warranted to determine the degree of variation, if any, that different segmentation software applications have on the proposed lung density measurements used in this profile. Consensus repeatability compiled from commercial and academic analysis software for RA-950 and Perc15 from a common reference data set (made available on the QIDW website) are summarized in Section 4.3 and tabulated in more detail in Appendix E.3.1.2 SpecificationParameterActorRequirementSample ProtocolAcquisition DeviceShall prepare a sample protocol conformant with Section 3.4.2 "Protocol Design Specification"Noise PerformanceAcquisition Device Shall demonstrate noise bias is ≤ ± 1 HU and standard deviation is ≤ 20 HU for lung equivalent foam (approximately -850 HU).See 4.1.2 Assessment Procedure: Voxel Noise and Noise Power Spectrum In-plane spatial resolution Acquisition DeviceShall demonstrate a Full-width at half-maximum (FWHM) ≤ 1.0 mm as described in Section 4.1.3.Through-plane spatial resolution Acquisition DeviceShall demonstrate a slice sensitivity profile withFWHM ≤ 1.0 mm as described in Section 4.1.4.Edge EnhancementAcquisition DeviceShall demonstrate an edge enhancement ≤ 3% for the edge response function as described in Section 4.1.3.Acquisition speedAcquisition DeviceShall be capable of whole lung coverage in < 10 s (i.e. a table feed of 3 cm/s or greater). CT radiation exposureAcquisition Device As prescribed in Section 3.4.2.Measured HU (Bias)Acquisition DeviceShall demonstrate a mean measured HU of -1000 HU ± 6 HU for inside air (within phantom), and 0 HU ± 6 HU for water (within phantom)*HU Stability (Repeatability)Acquisition DeviceShall demonstrate a standard deviation of ≤ 1 HU for inside air (within phantom), lung equivalent foam (within phantom), and water (within phantom) measured across N=5 acquisitionsLung Density AnalysisImage Analysis SoftwareShall be able to calculate and output for the whole lung:RA-950 HUPerc15Lung Density HistogramTotal Lung VolumeRepeatability Reproducibility of Analysis SoftwareImage Analysis SoftwareShall demonstrate a repeatability of …reproducibility of <0.72L for total lung volume, <2.6% for RA-950 and <3.9HU for Perc15 across different software vendors.See 4.3 Assessment Procedure: Repeatability Reproducibility of Analysis Software*This specification is only expected to apply in the COPD Gene, or similarly designed, phantom where uniform low density regions are provided. Both systemic and stochastic noise sources in patient data are expected to introduce bias and reduce precision. This additional variance is accounted for by the repeatability meta-analysis in patient studies used to define the Claims.3.2. Staff QualificationThis activity involves evaluating the human Actors (Radiologist, Physicist, and Technologist) prior to their participation in the Profile. It includes training, qualification or performance assessments that are necessary to reliably meet the Profile Claim.3.2.1 DiscussionThese requirements, as with any QIBA Profile requirements, are focused on achieving the Profile Claim. Evaluating the medical or professional qualifications of participating actors is beyond the scope of this profile. 3.2.2 SpecificationParameterActorSpecificationQualificationTechnologistShall be a trained radiation technologist as defined by ARRT.QualificationPhysicistShall be a Qualified Medical Physicist (QMP) as defined by AAPM.3.3. Periodic QAThis activity describes calibrations, phantom imaging, performance assessments or validations performed periodically at the site, but not directly associated with a specific subject, that are necessary to reliably meet the Profile Claim.3.3.1 Discussion Additional action may be needed if equipment no longer passes performance assessment.3.3.2 SpecificationParameterActorRequirementMonthly QAPhysicistShall evaluate the following parameters for each conformant acquisition device at least monthly or after equipment service that may alter its performance.Re-establishing StandardizationPhysicistShall, if the acquisition device fails Monthly QA, repeat Product Qualification (See 3.1.2) to re-establish standardization.Scanner CalibrationPhysicist or TechnologistShall assess the current CT conformance for HU value and standard deviation, encompassing scanning the COPD GeneCOPDGene phantom, and analyzing the images to prove conformance on a quarterly basis.Acquisition DeviceShall meet the specifications in Table 3.1.2.HU StabilityPhysicistShall confirm the longitudinal difference (i.e. drift) between subsequent monthly scans and baseline for air (within phantom) and water (within phantom) falls within± 4 HU3.4. Protocol DesignThis activity involves designing acquisition and reconstruction protocols for use in the Profile. It includes constraints on protocol acquisition and reconstruction parameters that are necessary to reliably meet the Profile Claim.3.4.1 DiscussionThe Profile considers Protocol Design to take place at the imaging site, however, sites may choose to make use of protocols developed elsewhere. Generalizable image quality specifications are favored over narrow pre-defined parameter settings to allow flexibility in developing and supporting quantitative density measures within a specified CT dose. Therefore, multiple possible parameter settings are allowed. The assessment procedures for spatial resolution and edge enhancement are performed using an appropriate test object (e.g. ACR or CATphan) to estimate the point or edge response function, and slice sensitivity profile PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5KdWR5PC9BdXRob3I+PFllYXI+MTk3NjwvWWVhcj48UmVj

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ADDIN EN.CITE.DATA 7-10 as described in Section 4.1.3 and 4.1.4. Similar assessment procedures for HU bias and repeatability (Section 4.1.1), and voxel noise (Section 4.1.2) require a COPD GeneCOPDGene phantom (See Section 4: “Assessment Procedures”, Figure 3) containing foam standards with lung equivalent foam densities. A representative example of a phantom (e.g. COPDGene phantom or equivalent) meeting or exceeding this standard is shown in Figure 3 and described further in Section 4.This approach is intended to enable different vendor architectures and reconstruction algorithms to meet the desired quantitative measurement standards while allowing flexibility to readily adapt protocols as CT systems continue to evolve.The approach of the specifications here is to focus as much as possible on the characteristics of the resulting dataset, rather than one particular technique for achieving those characteristics. This is intended to allow as much flexibility as possible for product innovation and reasonable adjustments for patient size (such as increasing acquisition mAs and reconstruction DFOV for larger patients), while reaching the performance targets. Again, the technique parameter sets in the Conformance Statements for Acquisition Devices and Reconstruction Software may be helpful for those looking for more guidance.The purpose of the minimum scan duration requirement is to permit acquisition of the lungs in a single breath-hold, thereby preventing respiratory motion artifacts or anatomic gaps between breath-holds.Pitch is chosen so as to allow completion of the scan in a single breath hold. Total Collimation Width (defined as the total nominal beam width, N x T, for example 64 detectors x 1.25mm thick) is often not directly visible in the scanner interface. Manufacturer reference materials typically explain how to determine this for a particular scanner make, model and operating mode. Wider collimation widths can increase coverage and shorten acquisition, but can introduce cone beam artifacts which may degrade image quality. Imaging protocols will seek to strike a balance to preserve image quality while providing sufficient coverage to keep acquisition times short. Nominal Tomographic Section Thickness (T), the term preferred by the International Electrotechnical Commission (IEC), is sometimes also called the Single Collimation Width. It affects the spatial resolution along the subject z-axis. Smaller voxels are preferable to reduce partial volume effects and provide higher accuracy due to higher spatial resolution. The resolution/voxel size that reaches the analysis software is affected by both acquisition parameters and reconstruction parameters.X-ray CT uses ionizing radiation. Exposure to radiation can pose risks; however, as the radiation dose is reduced, image quality can be degraded. It is expected that health care professionals will balance the need for good image quality with the risks of radiation exposure on a case-by-case basis. It is not within the scope of this document to describe how these trade-offs should be resolved, but it is strongly recommended that the CTDIvol be targeted to 3 mGy for an average sized patient (i.e. 75 kg) with the amount of radiation adjusted based on patient size and shape according to manufacturer. CT radiation dose in the chest is expected to vary by approximately ± 18% for subject weight between 50-100 kg ADDIN EN.CITE <EndNote><Cite><Author>Huda</Author><Year>2010</Year><RecNum>22</RecNum><DisplayText><style face="superscript">11</style></DisplayText><record><rec-number>22</rec-number><foreign-keys><key app="EN" db-id="ft099ww0vdaze9eepp05wda1000tsr0erf2r" timestamp="1520276523">22</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Huda, W.</author><author>Sterzik, A.</author><author>Tipnis, S.</author><author>Schoepf, U. J.</author></authors></contributors><auth-address>Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas Street, MSC 323, Charleston, South Carolina 29425-3230, USA.</auth-address><titles><title>Organ doses to adult patients for chest CT</title><secondary-title>Med Phys</secondary-title></titles><periodical><full-title>Med Phys</full-title></periodical><pages>842-7</pages><volume>37</volume><number>2</number><edition>2010/03/17</edition><keywords><keyword>Adult</keyword><keyword>*Body Burden</keyword><keyword>*Body Weight</keyword><keyword>Computer Simulation</keyword><keyword>Humans</keyword><keyword>Male</keyword><keyword>*Models, Biological</keyword><keyword>Radiography, Thoracic/*methods</keyword><keyword>Tomography, X-Ray Computed/*methods</keyword><keyword>*Viscera</keyword><keyword>Whole-Body Counting/*methods</keyword></keywords><dates><year>2010</year><pub-dates><date>Feb</date></pub-dates></dates><isbn>0094-2405 (Print)&#xD;0094-2405 (Linking)</isbn><accession-num>20229893</accession-num><urls><related-urls><url>, which is considered sufficiently small to be within the range of equivalent performance for the currentacceptable for the longitudinal claim but may be a source of additional variability for comparisons across different scanner makes and models, which is therefore omitted from the current claims of this profile (see “AEC” Open Issue)s as stated. Image reconstruction is modeled as a separate Activity in the QIBA Profile. Although it is closely related to image acquisition, and is usually performed on the Acquisition Device, reconstruction may be performed, or re-performed, separate from the acquisition. Many reconstruction parameters will be influenced or constrained by related acquisition parameters. This specification is the result of discussions to allow a degree of separation in their consideration without suggesting they are totally independent. Many reconstruction parameters can have direct or indirect effects on the lung density histogram used for computing parenchymal density measures. To reduce this potential source of variance, all efforts should be made to match acquisition and reconstruction parameters as with the baseline. Voxel noise (pixel standard deviation in a region of interest) can be reduced by reconstructing images with greater thickness for a given mAs. It is not expected that the Voxel Noise be measured for each subject scan, but rather the Acquisition Device and Reconstruction Software be qualified for the expected acquisition and reconstruction parameters as described in Section 4.2. The shape of the reconstruction kernel, or modulation transfer function (MTF), alters both the spatial resolution and noise characteristics of the image PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5GcmllZG1hbjwvQXV0aG9yPjxZZWFyPjIwMTM8L1llYXI+

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ADDIN EN.CITE.DATA 10,12. The reconstruction is a weighted sample of the structures within the projection. A smoother reconstruction kernel emphasizes larger structures in a projection by increasing their relative weight at the expense of smaller structures but with the benefit of reducing noise. So there is also an inverse relationship between spatial resolution and noise that is dependent on choice of reconstruction kernel, necessitating that the reconstruction kernel be carefully chosen to meet the specifications in Table 3.1.2 as recapitulated in the context of the human subject protocol in Table 3.4.2. Examples of kernels that would meet the specifications for the major vendors are described in Section 4.2. Note that specific constraints are not placed on most of the acquisition and reconstruction parameters in a protocol. It is presumed that significant changes to those parameters would result in non-conformant changes in Noise and Resolution. Changes that do not affect the Noise and Resolution are considered insignificant.3.4.2 SpecificationNote that the Radiologist is responsible for the protocol parameters, although they may choose to use a protocol provided by the vendor of the acquisition device. The Radiologist is also responsible for ensuring that the protocol has been validated, although the Physicist actor is responsible for performing the validation. The role of the Physicist actor may be played by an in-house medical physicist, a physics consultant or other staff (such as vendor service or specialists) qualified to perform the validations described. Protocol design should be done collaboratively between the Physicist and the Radiologist with the ultimate responsibility to the Radiologist. Some parameters are system dependent and may require special attention from the Physicist actor or equivalent.ParameterActorSpecificationDICOM TagAcquisition ProtocolRadiologistShall prepare a protocol to meet the specifications in this table.Shall ensure technologists have been trained on the requirements of this profile.Total Collimation WidthRadiologistTechnologistShall set to Greater than or equal to 16mm.Total Collimation Width(0018,9307)Nominal Tomographic Section ThicknessTechnologistRadiologistShall set to Less than or equal to 1mm.Single Collimation Width(0018,9306)Scan DurationTechnologistRadiologistShall achieve a table speed of at least 4cm per second (in order to cover the full lung within a 10s breath-hold).Table Speed(0018,9309)CT DoseTechnologistRadiologist≤ 3 mGy CTDIvol for a medium sized (75 kg) subject with the amount of radiation adjusted based on patient size and shape according to manufacturer. Dose ReportReconstruction ProtocolTechnologistRadiologistShall prepare a protocol to meet the specifications in this table.Shall ensure technologists have been trained on the requirements of this profile.Reconstructed Image ThicknessTechnologistRadiologistShall set to 1.0 mm or less.Slice Thickness (0018,0050)In-plane ResolutionPhysicistShall validate that the protocol achieves a full width at half maximum (FWHM) of line spread function ≤ 1 mm.Through-plane spatial resolution PhysicistShall validate that the protocol achieves a slice sensitivity profile withFWHM ≤ 1.0 mmEdge EnhancementPhysicistShall validate that the protocol achieves a minimum edge enhancement of 3% for the edge response function as described in Section 4.Voxel Noise PhysicistShall validate that the protocol achieves a standard deviation of voxel noise that is ≤ 20HU for lung equivalent foam, air and water materials inside a phantom as described in Section 4.3.5. Subject HandlingThis activity describes details of handling imaging subjects that are necessary to reliably meet the Profile Claim. 3.5.1 DiscussionSubject PositioningConsistent positioning avoids unnecessary variance in attenuation, location of subject within the scan gantry, and changes in anatomical shape due to posture, or body rotation that can affect image quality and consistency of HU value. Lung InflationAcquisition parameters have been specified to allow completion of the scan of the whole lung volume in a single breath-hold of less than 10 seconds. Faster scan time can further reduce breath-hold duration and reduce the likelihood of respiratory motion artifacts.Consistency of lung inflation volume is also critical to lung density measures. The specification is to achieve a difference in lung inflation smaller than 10% of baseline lung inflation volume for longitudinal time points with the goal of achieving greater than 90% of predicted TLC at both time points. To achieve consistency of breath-hold it is essential that the technologist perform consistent coaching of the subject before the CT acquisition (so that the subject is prepared for the voice commands while in the scanner). Before the scans are acquired, the coordinator (or trained CT technologist) will review the breathing instructions with the participant and emphasize the importance of following them as closely as possible during the actual imaging of the lungs. In summary, the participant will be instructed to inhale deeply and exhale 3 times and then hold their breath two different ways: with the lungs full of air (TLC scan). The technologist or coordinator should visually confirm that the subject is following the breath-hold coaching as intended (see script below). For example, as individual subjects will vary in their respiratory cycle and compliance with commands, it is important for the technologist or coordinator to give sufficient time for the subject to achieve full inspiration with visual confirmation that this is achieved by watching the subject’s chest before CT scanning commences. If additional expiratory scans are performed, please note that the cephalad/caudal coverage of the lungs from apex to base should be adjusted between the TLC and expiratory CT acquisitions to cover the lungs within the limits of the lung apex and base, e.g. no more than 2 cm cephalad to the apical or 5 cm caudal to the basal lung borders. An additional scout may be acquired between inspiratory and expiratory acquisitions, to prescribe each of the lung volume CT acquisitions separately so as to minimize CT dose to the subject. To extract the desired information from the CT images, it is very important that the breathing instructions are followed closely. Refer to this publication ADDIN EN.CITE <EndNote><Cite><Author>Newell</Author><Year>2013</Year><RecNum>8238</RecNum><DisplayText><style face="superscript">1</style></DisplayText><record><rec-number>8238</rec-number><foreign-keys><key app="EN" db-id="rwwtaedax0vw24evew7predtxx5rervs0dad" timestamp="0">8238</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Newell, J. D., Jr.</author><author>Sieren, J.</author><author>Hoffman, E. A.</author></authors></contributors><auth-address>Advanced Pulmonary Physiomics Imaging Laboratory, Department of Radiology, University of Iowa, Iowa City, IA 52242, USA. john-newell@uiowa.edu</auth-address><titles><title>Development of quantitative computed tomography lung protocols</title><secondary-title>J Thorac Imaging</secondary-title><alt-title>Journal of thoracic imaging</alt-title></titles><pages>266-71</pages><volume>28</volume><number>5</number><edition>2013/08/13</edition><dates><year>2013</year><pub-dates><date>Sep</date></pub-dates></dates><isbn>0883-5993</isbn><accession-num>23934142</accession-num><urls></urls><custom2>Pmc3876949</custom2><custom6>Nihms503532</custom6><electronic-resource-num>10.1097/RTI.0b013e31829f6796</electronic-resource-num><remote-database-provider>Nlm</remote-database-provider><language>eng</language></record></Cite></EndNote>1 for further visual and description information on proper breathing instructions. An example of a breath-hold coaching script is:“Take a deep breath in” (watch the chest to ensure deep breathe is achieved)“Let it out” (watch chest to insure exhale is achieved)“Take a deep breath in” (watch the chest to insure exhale is achieved and timing of breath cycle for the subject)“Let it out”“Now breathe all the way IN, IN, IN…” (watch to confirm timing and inhalation is fully achieved and chest is still)“Keep holding your breath – DO NOT BREATHE” Visually confirm inspiratory breath-hold by watching subject’s chest and commence CT scan.“Breathe and Relax.”3.5.2 SpecificationParameterActorRequirementSubject PositioningTechnologistShall place the subject in a supine position, arms positioned comfortably above the head in a head-arm rest with lower legs supported. Table HeightTechnologistShall adjust the table height for the mid-axillary plane of the chest to pass through the isocenter. Subject AlignmentTechnologistShall position the subject such that the “sagittal laser line” lies along the sternum (e.g. from the suprasternal notch to the xiphoid process).Scout ScansTechnologistShall perform a lateral scout and verify that the mid-axillary plane of the bronchial tree, at the level of the carina, is within 2 cm of iso-center.Shall perform an AP (or PA) scout and verify that the subject is correctly centered at horizontal iso-center within 2 cm. Lung CoverageTechnologistShall match the display field of view between time points to insure consistency of spatial resolution.Shall adjust the Cephalad/caudal coverage of the lungs from apex to base between the TLC and expiratory CT acquisitions to cover the lungs within the limits of the lung apex and base, e.g. no more than 2 cm cephalad to the apical or 5 cm caudal to the basal lung borders. Breath-holdCoachingTechnologistShall coach the subject on Breath-holding as specified above.Use of intravenous contrastRadiologist / TechnologistIntravenous contrast Shall NOT be used.Use of oral contrastRadiologist / TechnologistOral contrast Shall NOT be used.Artifact SourcesTechnologistShall remove or position potential sources of artifacts (specifically including breast shields, metal-containing clothing, EKG leads and other metal equipment) such that they will not degrade the reconstructed CT volumes.3.6. Image Data AcquisitionThis activity describes details of the data acquisition process that are necessary to reliably meet the Profile Claim. It may also include calibrations, performance assessments or validations during acquisition (such as visual confirmation of breath-hold) that are necessary to reliably meet the Profile Claim.3.6.1 DiscussionX-ray CT uses ionizing radiation, and exposure to ionizing radiation increases health risks to the subject. The CT Dose Index Volume (CTDIvol) is used to specify radiation exposure. FDA and international conformance standards require CTDIvol to be available on all CT platforms. The radiation exposure is determined by tube potential, source filtration, tube current-rotation time product, pitch, and total collimation width. The specifications of this profile are designed such that the CTDIvol be less than or equal to 3 mGy for an average-sized subject (75 kg) for each CT scan performed to minimize risk for longitudinal assessment of human subjects.3.6.2 SpecificationParameterActorRequirementDICOM TagAcquisition Protocol SelectionTechnologistShall select a protocol that has been previously prepared and validated for this purpose (See section 3.4.2 "Protocol Design Specification").Shall report if any parameters are modified beyond the specifications in section 3.4.2 "Protocol Design Specification".TechnologistIf acquiring a longitudinal time point, shall select a protocol on the same CT scanner with equivalent acquisition and reconstruction parameters to that of the baseline CT scan.Acquisition DeviceQualified DeviceProspective “Quantitative Tag”Scan PlaneTechnologistAxial / Transverse(0018,5100)Patient PositionScout (Topogram, Scanogram)TechnologistShall confirm the absence of metal or other artifacts Anatomic CoverageTechnologistShall ensure the Full Lung, from 2cm above the apex to 5cm below the base, is covered by the scanAxial field of viewTechnologistShall confirm the display field of view is no more than 2 cm outside maximal lung extent. Shall match the display field of view to that of the Baseline scan, if available.<Confirm DICOM Field> Reconstruction Field of View (0018,9317)(0018,1100)ReconstructionDiameterCT DoseRadiologist /Technologist≤ 3mGy CTDIvol for a 75kg subject allowing for increased/decreased CT dose adjusted based on patient size and shape according to manufacturer.(0018,9345) CTDICTDIvol3.7. Image Data ReconstructionThis activity describes criteria and procedures related to producing images from the acquired data that are necessary to reliably meet the Profile Claim.3.7.1 DiscussionThe shape of the reconstruction kernel is a component of the modulation transfer function (MTF) and alters both the spatial resolution and noise characteristics of the image PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5GcmllZG1hbjwvQXV0aG9yPjxZZWFyPjIwMTM8L1llYXI+

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ADDIN EN.CITE.DATA 10,12. A smoother reconstruction kernel emphasizes larger structures in a projection by increasing their relative weight at the expense of smaller structures but with the benefit of reducing noise. So there is also an inverse relationship between spatial resolution and noise that is dependent on choice of reconstruction kernel, necessitating that the reconstruction be carefully chosen to meet the specifications in Table 3.1.2 as recapitulated in the context of the human subject protocol in Table 3.4.2. Examples of kernels that would meet the specifications for the major vendors are described in Section 4.2. 3.7.2 SpecificationParameterActorRequirementDICOM TagReconstruction ProtocolTechnologistShall select a protocol that has been previously prepared and validated for this purpose (See section 3.4.2 "Protocol Design Specification").Shall report if any parameters are modified beyond those specifications.3.8. Image QAThis activity describes criteria and evaluations of the images that are necessary to reliably meet the Profile Claim.3.8.1 DiscussionAt the imaging console, subject images will be assessed for:Adequate coverage of the thoracic cavity; the lung volume must be fully represented in the field of view.Absence of respiratory motion artifact. Appropriate CT dose.Appropriate reconstruction algorithm and display field of view as specified.In conjunction with image analysis, the images will be further reviewed for the above issues, and additionally for the following:Absence of intravenous (IV) contrast.(If followup scan) adequacy of lung inflation- no more than 10% difference from baseline scan.Specified DICOM fields shall be monitored to confirm adherence to protocol and CT Dose as well as between baseline and followup scans.3.8.2 SpecificationParameterActorRequirementAdequate coverage Technologist Shall confirm the lung volume is fully represented in the field of viewMotionTechnologist Shall evaluate for respiratory motion (cardiac motion is unavoidable and acceptable) and confirm that the lung parenchyma is sufficiently clear and uncorrupted by motionSpatial resolution.TechnologistShall confirm the image headers (Single Slice Collimation (0018,9306) and Slice Thickness (0018,0050)) indicate the acquired and reconstructed resolutions ≤ 1 mmCT dose Technologist Shall confirm the image header (CTDIvol (0018,9345)) indicates the CTDIvol is “Per protocol.” Conformance to baseline.TechnologistShall confirm the Protocol is consistent with the baseline.Shall confirm the FOV (Data Collection Diameter (0018,0090)) are within 10% of baseline except when allowing for specific adjustment to accommodate substantial changes in subject size.Absence of IV contrastImage Analysis SoftwareShall confirm the absence of IV contrastLung VolumeImage Analysis SoftwareShall confirm the measured lung volume is within 10% of the baseline.3.9. Image DistributionImages shall be transmitted in uncompressed DICOM format and according to the anonymization standards approved for the study. 3.10. Image AnalysisThis activity describes criteria and procedures related to producing quantitative measurements from the images that are necessary to reliably meet the Profile Claim.3.10.1 DiscussionWith the advent of 3D volumetric CT, the field has moved towards full 3D volumetric segmentation of the lung. Given the typically large number of slices in a multi-slice volumetric CT lung scan, (400-600 slices), automated segmentation is a practical necessity. Accurate and reproducible automated segmentation of the lung structures requires combining several segmentation algorithms depending on the level of regional analysis required. For example, reproducible and accurate segmentation (compared to manual analysis) of the right and left lung can be readily performed using optimal thresholding followed by morphological operators and region growing as described by Hu and colleagues ADDIN EN.CITE <EndNote><Cite><Author>Hu</Author><Year>2001</Year><RecNum>6382</RecNum><DisplayText><style face="superscript">13</style></DisplayText><record><rec-number>6382</rec-number><foreign-keys><key app="EN" db-id="rwwtaedax0vw24evew7predtxx5rervs0dad" timestamp="0">6382</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Hu, S.</author><author>Hoffman, E. A.</author><author>Reinhardt, J. M.</author></authors></contributors><auth-address>Department of Biomedical Engineering, University of Iowa, Iowa City 52242, USA.</auth-address><titles><title>Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images</title><secondary-title>IEEE Trans Med Imaging</secondary-title></titles><pages>490-8</pages><volume>20</volume><number>6</number><keywords><keyword>Algorithms</keyword><keyword>Humans</keyword><keyword>*Imaging, Three-Dimensional</keyword><keyword>Lung/anatomy &amp; histology/*radiography</keyword><keyword>Radiographic Image Interpretation, Computer-Assisted/methods</keyword><keyword>Reproducibility of Results</keyword><keyword>Research Support, Non-U.S. Gov&apos;t</keyword><keyword>Research Support, U.S. Gov&apos;t, P.H.S.</keyword><keyword>Tomography, X-Ray Computed/*methods</keyword></keywords><dates><year>2001</year><pub-dates><date>Jun</date></pub-dates></dates><accession-num>11437109</accession-num><urls><related-urls><url> </url></related-urls></urls></record></Cite></EndNote>13. Other methods, such as statistical shape modeling and atlas-based segmentation have been proposed ADDIN EN.CITE <EndNote><Cite><Author>Sluimer</Author><Year>2006</Year><RecNum>16</RecNum><DisplayText><style face="superscript">14</style></DisplayText><record><rec-number>16</rec-number><foreign-keys><key app="EN" db-id="ft099ww0vdaze9eepp05wda1000tsr0erf2r" timestamp="1502739226">16</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Sluimer, I.</author><author>Schilham, A.</author><author>Prokop, M.</author><author>van Ginneken, B.</author></authors></contributors><auth-address>Image Sciences Institute, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands. ingrid@isi.uu.nl</auth-address><titles><title>Computer analysis of computed tomography scans of the lung: a survey</title><secondary-title>IEEE Trans Med Imaging</secondary-title></titles><periodical><full-title>IEEE Trans Med Imaging</full-title></periodical><pages>385-405</pages><volume>25</volume><number>4</number><edition>2006/04/13</edition><keywords><keyword>*Algorithms</keyword><keyword>Animals</keyword><keyword>Artificial Intelligence</keyword><keyword>Humans</keyword><keyword>Image Enhancement/methods</keyword><keyword>Imaging, Three-Dimensional/*methods</keyword><keyword>Information Storage and Retrieval/methods</keyword><keyword>Lung/*diagnostic imaging</keyword><keyword>Lung Diseases/*diagnostic imaging</keyword><keyword>Pattern Recognition, Automated/*methods</keyword><keyword>Radiographic Image Interpretation, Computer-Assisted/*methods</keyword><keyword>Reproducibility of Results</keyword><keyword>Sensitivity and Specificity</keyword><keyword>Tomography, X-Ray Computed/*methods</keyword></keywords><dates><year>2006</year><pub-dates><date>Apr</date></pub-dates></dates><isbn>0278-0062 (Print)&#xD;0278-0062 (Linking)</isbn><accession-num>16608056</accession-num><urls><related-urls><url> addition to open source and academic segmentation software, sSeveral commercial and prototype commercial packages are becoming available for these analyses (e.g. VIDA, Imbio, Thirona, MeVIS), each with their own proprietary segmentation method. As RA-950 and Perc15 are both straight-forward deterministic computational operations, the primary source of variation in the analysis software lies in the lung volume segmentation mask used to compute the normalizing lung volume, and the removal of the trachea and major airways and vessels and correction of artifacts to a lesser extent.. Because ofDue to the multitude of software programs used by different commercial, open-source, and academic research labs, an evaluation is warranted to determine the degree of variation, if any, that different segmentation software applications have on the proposed lung density measurements used in this profile. For example, in longitudinal analysis of CT images that uses different software vendors at different time-points, measurement variability due to the differences in vendor segmentation will be introduced. Consensus repeatability reproducibility compiled from various commercial and academic analysis software for RA-950 and Perc15 from a common reference data set (made available on the QIDW website) are summarized in Section 4.32, and tabulated in more detail in Appendix E.3.10.2 SpecificationParameterActorRequirementLung Density Analysis Image Analysis SoftwareShall use a consistent lung segmentation procedure including the following steps:Segmentation and removal of central pulmonary blood vessels.Segmentation and removal of the central airways. Generation of the image histogram for the remaining lung parenchymal tissuesShall demonstrate a reproducibility coefficient (RDC) across vendors for total lung volume of ≤ 0.5172 L using the QIBA reference CT data set (N = 10050 curated chest CT’s in patients with COPD at standard dose) available for download upon request at the quantitative imaging data wharehouse ( HYPERLINK "" QIDW). See section 4.2Image Analysis SoftwareShall calculate and output the whole lung the RA-950, Perc15 and Lung Density Histogram.Shall demonstrate an RDC for repeated calculations of RA-950HU, and Perc15 of ≤ 1.82.6 % and 2.43.9 HU, respectively, using the QIBA reference CT data set (N = 10050 curated chest CT’s in patients with COPD) available for download upon request at the quantitative imaging data wharehouse ( HYPERLINK "" QIDW).3.11. Image InterpretationThis activity describes criteria and procedures related to clinically interpreting the measurements and images that are necessary to reliably meet the Profile Claim.3.11.1 DiscussionMeasured changes in lung attenuation in individuals may be compared with the previously published mean changes; for example the mean change in volume adjusted lung density in untreated subjects with Alpha-1 antitrypsin deficiency is about 2.2 g/L/year PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5EaXJrc2VuPC9BdXRob3I+PFllYXI+MjAwOTwvWWVhcj48

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ADDIN EN.CITE.DATA 3, and the mean change in cigarette smokers with COPD is about 1.1 g/L/year PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Db3hzb248L0F1dGhvcj48WWVhcj4yMDEzPC9ZZWFyPjxS

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bmROb3RlPn==

ADDIN EN.CITE PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Db3hzb248L0F1dGhvcj48WWVhcj4yMDEzPC9ZZWFyPjxS

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ADDIN EN.CITE.DATA 5. Important potential biological confounders in measurement of lung attenuation should also be considered. These would include significant changes in inspiratory lung volume, presence of other significant lung diseases on baseline or followup scans (e.g. pneumonia, interstitial lung disease), intervening surgery, and change in smoking status. Importantly, smoking cessation decreases Perc 15Perc15 lung attenuation by a mean of 4.9 HU, simulating progression of emphysema PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TaGFrZXI8L0F1dGhvcj48WWVhcj4yMDExPC9ZZWFyPjxS

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ADDIN EN.CITE.DATA 15.3.11.2 SpecificationParameterActorRequirementReal Change?Clinician or StatisticianDetermine if disease progressed or is stable.Magnitude of ChangeClinician or StatisticianDetermine if magnitude of the change is or is not significant with respect to the hypothesis of the proposed study.-1104904577080Figure 3. CT slice image of the “COPD Gene” lung phantom used in this study. The phantom consists of (A) 4 lb/ft3, (B) acrylic, (C) 12 lb/ft3, (D) water, (E) 20 lb/ft3, (F) air, and (G) a larger oval shaped lung density equivalent foam surrounded by an outer chest wall equivalent uniformity ring.00Figure 3. CT slice image of the “COPD Gene” lung phantom used in this study. The phantom consists of (A) 4 lb/ft3, (B) acrylic, (C) 12 lb/ft3, (D) water, (E) 20 lb/ft3, (F) air, and (G) a larger oval shaped lung density equivalent foam surrounded by an outer chest wall equivalent uniformity ring.4. Assessment ProceduresMost of the requirements described in Section 3 can be assessed for conformance by direct observation, however some of the performance-oriented requirements are assessed using a procedure. Specific assessment procedures that are required, or need further clarification, are defined in subsections below and the subsection is referenced not in numerical order, but from the corresponding requirement in Section 3.Below are specific examples of assessment procedures that meet the standards of this profile. In each case a specific measurement procedure is outlined using the COPD GeneCOPDGene phantom (Figure 3) that is sufficient to meet the minimum standards for scanner qualification (Section 3. 1) and periodic quality assurance (Section 3.2) and is published and commercially available (the COPD GeneCOPDGene phantom, The Phantom Laboratory - ) PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TaWVyZW48L0F1dGhvcj48WWVhcj4yMDEyPC9ZZWFyPjxS

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ADDIN EN.CITE.DATA 16,17. For more advanced studies in which standardization is required across a network of sites, an ideal reference object would include a series of at least five foam standards whose density is in the range of lung parenchyma (64-321 kg/m3) that have been calibrated to their true densities. Such a phantom is introduced and described in PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5DaGVuLU1heWVyPC9BdXRob3I+PFllYXI+MjAxNzwvWWVh

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ADDIN EN.CITE.DATA 6 and has been shown to establish a HU-electron density relationship for a given scanner and protocol. Related methods ADDIN EN.CITE <EndNote><Cite><Author>Martinez</Author><Year>2012</Year><RecNum>24</RecNum><DisplayText><style face="superscript">18</style></DisplayText><record><rec-number>24</rec-number><foreign-keys><key app="EN" db-id="ft099ww0vdaze9eepp05wda1000tsr0erf2r" timestamp="1520346607">24</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Martinez, L. C.</author><author>Calzado, A.</author><author>Rodriguez, C.</author><author>Gilarranz, R.</author><author>Manzanas, M. J.</author></authors></contributors><auth-address>Medical Physics and Radiation Protection Service, 12 de Octubre University Hospital, Madrid, Spain. lmartinezg.hdoc@salud.</auth-address><titles><title>A parametrization of the CT number of a substance and its use for stoichiometric calibration</title><secondary-title>Phys Med</secondary-title></titles><periodical><full-title>Phys Med</full-title></periodical><pages>33-42</pages><volume>28</volume><number>1</number><edition>2011/03/23</edition><keywords><keyword>Calibration</keyword><keyword>Linear Models</keyword><keyword>Photons</keyword><keyword>Radiotherapy Planning, Computer-Assisted</keyword><keyword>Tomography, X-Ray Computed/*standards</keyword></keywords><dates><year>2012</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>1724-191X (Electronic)&#xD;1120-1797 (Linking)</isbn><accession-num>21419682</accession-num><urls><related-urls><url> may also be considered for harmonization, although this is not strictly necessary to meet the longitudinal claims.Sites or vendors may also submit to QIBA, proposed alternative methods with evidence that the results produced by the proposed method are equivalent to the described reference methods. Upon review and approval by QIBA, the alternative method will also become an accepted assessment procedure in this Profile.The test procedure described here is based on the use of conventional filtered backprojection reconstruction methods; extreme care must be taken when iterative reconstruction methods are used as their use may invalidate some of the assumptions inherent in this method and are considered open issues for the present status of this Profile.Several CT protocols are also given in a link within Appendix D and on the QIBA wiki page (<QIBA wiki Excel link>) that are derived from the proposed procedures and would meet the claims of this profile. Acquisition parameters have been specified to allow completion of the scan of the whole lung volume in a single breath-hold of less than 10 seconds. Faster scan time can further reduce breath-hold duration and reduce the likelihood of respiratory motion artifacts. This procedure is recommended to be used by a physicist at the vendor origin or at an imaging site, to insure product qualification for a CT scanner in terms of linearity and repeatability of the measurement of CT number for air, lung equivalent foam, and water standards. For the minimum standard, the following procedure can be followed for image acquisition: 1. The assessor shall first warm up the scanner’s x-ray tube and perform calibration scans (often called air-calibration scans) according to scanner manufacturer recommendations.2. It is expected that scout (topogram, scanigram, etc.) images will be initially obtained to optimize positioning and coverage of an imaging phantom. 3. The assessor shall then scan a phantom of containing regions of uniform low density, such as the lung equivalent foam regions of the COPD GeneCOPDGene phantom PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TaWVyZW48L0F1dGhvcj48WWVhcj4yMDEyPC9ZZWFyPjxS

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ADDIN EN.CITE.DATA 16,17. The phantom shall be placed at the isocenter of the scanner. The acquisition protocol and reconstruction parameters shall conform to this Profile (See Section 3.6.2 and 3.7.2). The same protocol and parameters shall be used when performing the assessments in 4.1.1, 4.1.2 and 4.1.3.4.1.1 Assessment Procedure: HU Bias and RepeatabilityThe procedure for measuring water and air density in the COPD Gene phantom is performed as follows:As for any phantom calibration in CT, it is critical to position the phantom at the center of gantry rotation and in alignment with the axial scan plane such that the standards are minimally affected by differences in magnification and resolution. This can be achieved by using fiducial markers on the phantom and the alignment system of the scanner itself. Scanning should be performed for N = 5 repeated realizations (i.e. repeated acquisitions of the phantom) in order to measure the noise and standard deviation of mean HU values in the procedures below and according to the specifications in Table 3.1.2. After the scan is performed, the assessor shall place an approximately cylindrical region of interest (ROI) within a central axial slice of the phantom to include the uniform volume of the material to be measured (e.g. inside water and air, Figure 3D and F, respectively) positioned such that partial volume effects will NOT impact the measurement. An ROI shall be drawn to avoid the outer 2 mm of the cylindrical circumference but containing at least 3,000 voxels for these measurements. Standard commercial and opens source software packages that are sufficient for ROI placement and measurement are published ADDIN EN.CITE <EndNote><Cite><Author>J. Guo</Author><Year>2008</Year><RecNum>9</RecNum><DisplayText><style face="superscript">19</style></DisplayText><record><rec-number>9</rec-number><foreign-keys><key app="EN" db-id="ft099ww0vdaze9eepp05wda1000tsr0erf2r" timestamp="1502737857">9</key></foreign-keys><ref-type name="Conference Proceedings">10</ref-type><contributors><authors><author>J. Guo, </author><author>M. K. Fuld, </author><author>S. K. Alford, </author><author>J. M. Reinhardt, </author><author>E. A. Hoffman.</author></authors><secondary-authors><author>In M. Brown, M. de Bruijne, B. van Ginneken, A. Kiraly, J.-M. Kuhnigk, C. Lorenz, K. Mori, and J. M. Reinhardt, eds.</author></secondary-authors></contributors><titles><title>Pulmonary Analysis Software Suite 9.0: Integrating quantitative measures of function with structural analyses.</title><secondary-title>First International Workshop on Pulmonary Image Analysis</secondary-title></titles><pages>283-292</pages><dates><year>2008</year></dates><pub-location>New York</pub-location><urls></urls></record></Cite></EndNote>19, [e.g. ].The assessor shall record the values reported for the ROI mean and standard deviation of the HU and insure they meet the specifications in Table 3.1.2. Specifications are restated here for convenience and include: 1000 HU ±± 6 HU for inside air (within phantom), and 0 HU ± 6 HU for water (within phantom), and for repeated scans across N=5 acquisitions, a standard deviation of ≤ 1 HU for inside air (within phantom), lung equivalent foam (within phantom), and water (within phantom).4.1.2 Assessment Procedure: Voxel Noise and Noise Power SpectrumThis procedure can be used by a vendor or an imaging site to assess the voxel noise of reconstructed images and noise power as a function of spatial frequency, i.e. the noise power spectrum. For voxel noise assessment, use the same scanning procedure and images acquired as in 4.1.1 above.The procedure for measuring voxel noise is performed as follows: Subtract two unique realizations of the phantom and place a cylindrical ROI in the center of the phantom in a uniform regions corresponding to the lung density equivalent foam insert regions (Figure 3G). ROI placement and size should meet the minimum standards in 4.1.1 above.The assessor shall record the values reported for the ROI mean and standard deviation and insure they meet the specifications for noise bias and standard deviation specified in Table 3.1.2. Specifications are restated here for convenience and include:noise bias in the subtracted image is ≤ ± 1 HU, and the standard deviation of the noise is ≤ 20 HU.Given the proprietary nature of CT reconstruction kernels, some care in comparing reconstruction kernels, or “algorithms”, used for reconstruction across vendors is warranted. A preferred, but more involved, method that could beThe vendor shall employed during specify that the reconstruction kernel for the proposed protocol design (Section 3.4 and Table 3.4.2) is matched as best as possible to the spatial frequency dependence of the noise behavior, i.e. the noise power spectra (NPS), of typical smooth kernels such as the GE Standard, Siemens B31f or B34f, Philips B, and Cannon FC17 kernels. protocol design (Section 3.4 and Table 3.4.2) is to calculate the noise power spectrum (NPS), i.e. the Fourier transform of the auto correlation function, which fully describes the spatial frequency dependence of the noise behavior. Given the proprietary nature of CT reconstruction kernels, the only way to robustly compare reconstruction kernels, or “algorithms”, used for reconstruction across vendors is to calculate the NPS, ; such an approach andMethods for calculating the NPS methods are published PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5GcmllZG1hbjwvQXV0aG9yPjxZZWFyPjIwMTM8L1llYXI+

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ADDIN EN.CITE.DATA 10,12. For multi-center protocols that use different CT vendors in particular, calculation of the NPS would be preferredand certification by the vendor so as tothat all reconstruction kernels match as best as possible the spatial frequency dependence of the noise behavior for different scanner makes and models. For example, typical smooth reconstruction kernels such as the GE Standard and Siemens B31f or B43f are well matched with respect to their NPS is critical. O, but other pairings and novelfuture reconstruction kernels or acceptable , could be selected assuming they meet the specifications outlined in Section 3.1 and 3.4, are sufficiently smooth, and are matched throughout the network.A general procedure sufficient for measuring the noise power spectrum is described has been described ADDIN EN.CITE <EndNote><Cite><Author>Friedman</Author><Year>2013</Year><RecNum>21</RecNum><DisplayText><style face="superscript">10</style></DisplayText><record><rec-number>21</rec-number><foreign-keys><key app="EN" db-id="ft099ww0vdaze9eepp05wda1000tsr0erf2r" timestamp="1519593740">21</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Friedman, S. N.</author><author>Fung, G. S.</author><author>Siewerdsen, J. H.</author><author>Tsui, B. M.</author></authors></contributors><auth-address>Sackler School of Medicine, Faculty of Medicine, Tel Aviv University, Ramat Aviv 69978, Israel. saul.friedman@</auth-address><titles><title>A simple approach to measure computed tomography (CT) modulation transfer function (MTF) and noise-power spectrum (NPS) using the American College of Radiology (ACR) accreditation phantom</title><secondary-title>Med Phys</secondary-title></titles><periodical><full-title>Med Phys</full-title></periodical><pages>051907</pages><volume>40</volume><number>5</number><edition>2013/05/03</edition><keywords><keyword>*Accreditation</keyword><keyword>Adult</keyword><keyword>Humans</keyword><keyword>*Phantoms, Imaging</keyword><keyword>Signal-To-Noise Ratio</keyword><keyword>*Societies, Medical</keyword><keyword>Tomography, X-Ray Computed/*instrumentation</keyword></keywords><dates><year>2013</year><pub-dates><date>May</date></pub-dates></dates><isbn>2473-4209 (Electronic)&#xD;0094-2405 (Linking)</isbn><accession-num>23635277</accession-num><urls><related-urls><url> Assessment Procedure: In-Plane, Through-Plane (Z-axis) Spatial Resolution and Edge EnhancementThis procedure is recommended to be used byThe vendor shallould specifyconfirm that the slice profile, point spread and edge response functions, PSF and ERF respectively, meet the standards specified for scanner qualification (Section 3.1 and Table 3.1.2) and a physicist at the vendor origin or at an imaging site, to insure spatial resolution and edge enhancement are within adequate range for protocol design (Section 3.4 and Table 3.4.2). The in-plane point response function shall be measured using one of the following methods: using the derivative of the edge response function (ERF) measured from the oversampled outside edge of, for example, the COPD Gene phantom as in the reference by Judy ADDIN EN.CITE <EndNote><Cite><Author>Judy</Author><Year>1976</Year><RecNum>20</RecNum><DisplayText><style face="superscript">7</style></DisplayText><record><rec-number>20</rec-number><foreign-keys><key app="EN" db-id="ft099ww0vdaze9eepp05wda1000tsr0erf2r" timestamp="1519593667">20</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Judy, P. F.</author></authors></contributors><titles><title>The line spread function and modulation transfer function of a computed tomographic scanner</title><secondary-title>Med Phys</secondary-title></titles><periodical><full-title>Med Phys</full-title></periodical><pages>233-6</pages><volume>3</volume><number>4</number><edition>1976/07/01</edition><keywords><keyword>Diagnosis, Computer-Assisted</keyword><keyword>Technology, Radiologic</keyword><keyword>Tomography, X-Ray/*instrumentation</keyword></keywords><dates><year>1976</year><pub-dates><date>Jul-Aug</date></pub-dates></dates><isbn>0094-2405 (Print)&#xD;0094-2405 (Linking)</isbn><accession-num>785200</accession-num><urls><related-urls><url>, or using standard CT performance phantoms such as the ACR phantom or CATphan. The derived point response function shall meet the specifications outlined for spatial resolution in Table 3.1.2. Specifications are restated here for convenience and include:The point response function shall demonstrate a Full-width at half-maximum (FWHM) ≤ 1.0 mm.Procedures for assessing PSF and and ERF has been published using the derivative of the edge response function (ERF) measured from the oversampled outside edge of, for example, the COPD Gene phantom as in the reference by Judy ADDIN EN.CITE <EndNote><Cite><Author>Judy</Author><Year>1976</Year><RecNum>20</RecNum><DisplayText><style face="superscript">7</style></DisplayText><record><rec-number>20</rec-number><foreign-keys><key app="EN" db-id="ft099ww0vdaze9eepp05wda1000tsr0erf2r" timestamp="1519593667">20</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Judy, P. F.</author></authors></contributors><titles><title>The line spread function and modulation transfer function of a computed tomographic scanner</title><secondary-title>Med Phys</secondary-title></titles><periodical><full-title>Med Phys</full-title></periodical><pages>233-6</pages><volume>3</volume><number>4</number><edition>1976/07/01</edition><keywords><keyword>Diagnosis, Computer-Assisted</keyword><keyword>Technology, Radiologic</keyword><keyword>Tomography, X-Ray/*instrumentation</keyword></keywords><dates><year>1976</year><pub-dates><date>Jul-Aug</date></pub-dates></dates><isbn>0094-2405 (Print)&#xD;0094-2405 (Linking)</isbn><accession-num>785200</accession-num><urls><related-urls><url>, or using standard CT performance phantoms such as the ACR phantom or CATphan.Using either phantom, tThe procedure for measuring Edge Enhancement (EE) shall be performed as follows:EE shall be derived from the edge response function according to the equation:EE (%)=100EEmEEr-1,Where EEm is the maximum observed contrast along the ERF and EEr is the reference value calculated as the mean HU value within a uniform region of the edge material (e.g. the outer chest wall equivalent uniformity ring). The derived response functions shall meet the specifications outlined for edge response in Table 3.1.2. Specifications are restated here for convenience and include:Maximum edge enhancement 3%.4.1.4 Assessment Procedure: Through-Plane (Z-axis) Spatial Resolution Using the CATphan or similar, tThe procedure for measuring through-plane resolution (slice profile) shall be performed as follows:The through-plane resolution shall be assessed using the slice sensitivity profile using methods described in Fuchs et al. ADDIN EN.CITE <EndNote><Cite><Author>Fuchs</Author><Year>2000</Year><RecNum>11</RecNum><DisplayText><style face="superscript">8</style></DisplayText><record><rec-number>11</rec-number><foreign-keys><key app="EN" db-id="ft099ww0vdaze9eepp05wda1000tsr0erf2r" timestamp="1502738183">11</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Fuchs, T.</author><author>Krause, J.</author><author>Schaller, S.</author><author>Flohr, T.</author><author>Kalender, W. A.</author></authors></contributors><auth-address>Institute of Medical Physics, Erlangen, Germany. theo@imp.uni-erlangen.de</auth-address><titles><title>Spiral interpolation algorithms for multislice spiral CT--part II: measurement and evaluation of slice sensitivity profiles and noise at a clinical multislice system</title><secondary-title>IEEE Trans Med Imaging</secondary-title></titles><periodical><full-title>IEEE Trans Med Imaging</full-title></periodical><pages>835-47</pages><volume>19</volume><number>9</number><edition>2000/12/29</edition><keywords><keyword>*Algorithms</keyword><keyword>*Image Processing, Computer-Assisted</keyword><keyword>Sensitivity and Specificity</keyword><keyword>*Tomography, X-Ray Computed</keyword></keywords><dates><year>2000</year><pub-dates><date>Sep</date></pub-dates></dates><isbn>0278-0062 (Print)&#xD;0278-0062 (Linking)</isbn><accession-num>11127599</accession-num><urls><related-urls><url> and with standard procedures with CT performance phantoms such as the ACR or CATphan phantoms. The derived slice sensitivity profile shall meet the specifications outlined for through-plane resolution in Table 3.1.2. Specifications are restated here for convenience and include:Shall demonstrate a Full-width at half-maximum (FWHM) ≤ 1.0 mm.4.2. Assessment Procedure: Bias Reproducibility of Image Analysis Software across Various VendorsTo qualify new analysis software, a reference data set shall be analyzed and results compared to performance of established commercial and open source software packages. The reference data set is made available on the for download upon request at the quantitative imaging data wharehouse ( HYPERLINK "" QIDW). QIDW <URL here>. The reference data set consists of 100 50 cases with varying degrees of airflow limitation, ranging from never-smokers with normal pulmonary function (n=10) to GOLD IV COPD (n=10 cases in each GOLD group), and including both conventional (~6 mGy average CTDIvol) and lowreduced dose (~3 mGy average CTDIvol) CT data sets from the same subjects. Download and analysis shall measure segmented total lung volume and, and RA-950 and Perc15 from the lung density histogram [two or more times] to establish if the proposed analysis software meets standards for bias reproducibility compared to established commercial and open source software packages. Acceptable limits for bias reproducabilityreproducibility were determined from a prior study incorporating 98X vendors and open source academic centers that usedran their version of lung segmentation and density measurement, following the guidelines outlined above. The vendors and submitted their results to the University of British Columbia (Vancouver, BC, Canada), who ran thewhere the central image analysis was performed. Lung density measurements from each vendor were then compared to one all other vendors, another and to average measurements using Bland-Altman analysis, and the results are tabulated in Appendix E. To qualify a new vendor software tool (“Vendor A”), the performance for the new software tool must be compared to all the other measurements. We define ?2 as the "mean of the variances of repeated measurements [i.e. algorithms] on the same patient. Specifically, “Vendor A” results for RA-950 and/or Perc15 will be compared with all the other vendors included in this Profile for a total of 7 comparisons, which can be used to estimate a single variance, ?2. The ?2 is computed by taking the mean of all those variances over 50 subjects to give the RDC for Vendor A, where RDC is is defined here under the assumption of normality as 1.96 times the standard deviation (SD); the equation is thus formulated as RDC = 1.96 * sqrt (2 * ?2).From this data and analysis, the following thresholds for performance of lung segmentation, RA-950 and Perc15 using the reference data set were established using the upper 95% confidence intervals of the reproducibility coefficient (RDC) generated on repeated measurements of the same subjects from the reference data set using different software vendors: The upper bound of the 95% CIs for the bias ofRepeated measurements of a subject using different software vendors for measurement of RA-950 and Perc15total lung volume shall be less than x%0.5172 L (See Appendix F: Tables 1 and 2)<Lung segmentation specification here> Repeated measurements of a subject using different software vendors for measurement of The upper bound of the 95% CIs for the repeatabilityreproducability coefficient of RA-950 and Perc15 shall be less than 1.82.6 % and 3.92.4 HU, respectivelyxx. (Appendix F: Tables 1 and 2). <RA-950 and Perc15 specifications here> Nancy Obuchowski comments here. 5. ConformanceTo conform to this Profile, participating staff and equipment (“Actors”) shall support each activity assigned to them in Table 1 in Section 3. To support an activity, the actor shall conform to the requirements (indicated by “shall language”) listed in the Specifications table of the activity. Each activity has a dedicated subsection in Section 3. For convenience, the Specification table requirements have been duplicated and regrouped by actor in the form of a checklist in Appendix E. Some requirements reference a specific assessment procedure in section 4 that shall be used to assess conformance to that requirement.If a QIBA Conformance Statement is already available for an actor (e.g. your analysis software), you may choose to provide a copy of that statement rather than confirming each of the requirements in that Actors checklist yourself.Formal claims of conformance by the organization responsible for an Actor shall be in the form of a published QIBA Conformance Statement. Vendors publishing a QIBA Conformance Statement shall provide a set of “Model-specific Parameters” (as shown in Appendix D) describing how their product was configured to achieve conformance. Vendors shall also provide access or describe the characteristics of the test set used for conformance testing. References ADDIN EN.REFLIST 1.Newell JD, Jr., Sieren J, Hoffman EA. Development of quantitative computed tomography lung protocols. J Thorac Imaging. 2013;28(5):266-271.2.Sieren JP, Newell JD, Jr., Barr RG, et al. SPIROMICS Protocol for Multicenter Quantitative Computed Tomography to Phenotype the Lungs. Am J Respir Crit Care Med. 2016;194(7):794-806.3.Dirksen A, Piitulainen E, Parr DG, et al. Exploring the role of CT densitometry: a randomised study of augmentation therapy in alpha1-antitrypsin deficiency. Eur Respir J. 2009;33(6):1345-1353.4.Chapman KR, Burdon JG, Piitulainen E, et al. Intravenous augmentation treatment and lung density in severe alpha1 antitrypsin deficiency (RAPID): a randomised, double-blind, placebo-controlled trial. Lancet. 2015;386(9991):360-368.5.Coxson HO, Dirksen A, Edwards LD, et al. The presence and progression of emphysema in COPD as determined by CT scanning and biomarker expression: a prospective analysis from the ECLIPSE study. Lancet Respir Med. 2013;1(2):129-136.6.Chen-Mayer HH, Fuld MK, Hoppel B, et al. Standardizing CT lung density measure across scanner manufacturers. Med Phys. 2017;44(3):974-985.7.Judy PF. The line spread function and modulation transfer function of a computed tomographic scanner. Med Phys. 1976;3(4):233-236.8.Fuchs T, Krause J, Schaller S, Flohr T, Kalender WA. Spiral interpolation algorithms for multislice spiral CT--part II: measurement and evaluation of slice sensitivity profiles and noise at a clinical multislice system. IEEE Trans Med Imaging. 2000;19(9):835-847.9.Zhang J, Bruesewitz MR, Bartholmai BJ, McCollough CH. Selection of appropriate computed tomographic image reconstruction algorithms for a quantitative multicenter trial of diffuse lung disease. J Comput Assist Tomogr. 2008;32(2):233-237.10.Friedman SN, Fung GS, Siewerdsen JH, Tsui BM. A simple approach to measure computed tomography (CT) modulation transfer function (MTF) and noise-power spectrum (NPS) using the American College of Radiology (ACR) accreditation phantom. Med Phys. 2013;40(5):051907.11.Huda W, Sterzik A, Tipnis S, Schoepf UJ. Organ doses to adult patients for chest CT. Med Phys. 2010;37(2):842-847.12.Solomon J, Samei E. Quantum noise properties of CT images with anatomical textured backgrounds across reconstruction algorithms: FBP and SAFIRE. Med Phys. 2014;41(9):091908.13.Hu S, Hoffman EA, Reinhardt JM. Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. IEEE Trans Med Imaging. 2001;20(6):490-498.14.Sluimer I, Schilham A, Prokop M, van Ginneken B. Computer analysis of computed tomography scans of the lung: a survey. IEEE Trans Med Imaging. 2006;25(4):385-405.15.Shaker SB, Stavngaard T, Laursen LC, Stoel BC, Dirksen A. Rapid fall in lung density following smoking cessation in COPD. COPD. 2011;8(1):2-7.16.Sieren JP, Newell JD, Judy PF, et al. Reference standard and statistical model for intersite and temporal comparisons of CT attenuation in a multicenter quantitative lung study. Med Phys. 2012;39(9):5757-5767.17.Guo J, Wang C, Chan KS, et al. A controlled statistical study to assess measurement variability as a function of test object position and configuration for automated surveillance in a multicenter longitudinal COPD study (SPIROMICS). Med Phys. 2016;43(5):2598.18.Martinez LC, Calzado A, Rodriguez C, Gilarranz R, Manzanas MJ. A parametrization of the CT number of a substance and its use for stoichiometric calibration. Phys Med. 2012;28(1):33-42.19.J. Guo, M. K. Fuld, S. K. Alford, J. M. Reinhardt, Hoffman. EA. Pulmonary Analysis Software Suite 9.0: Integrating quantitative measures of function with structural analyses. Paper presented at: First International Workshop on Pulmonary Image Analysis2008; New York.AppendicesAppendix A: Acknowledgements and AttributionsThis Profile is a joint effort of the QIBA Lung Density Biomarker Committee* under the supervision of Phil Judy, Heather Chen-Mayer, Sean B. Fain, David Lynch, and Matthew K. Fuld. Discussions and feedback from Nancy Obuchowski greatly improved the statistical methods used in the meta-analysis. Discussions and editorial comments from Greg Kinney, and Ella Kazerooni improved the presentation to better support the profile.*Committee members: Sean B. Fain, David Lynch, Matthew Fuld, Philip Judy, Heather Chen-Mayer, Stephen Humphries, Bernice Hoppel, Charles Hatt, Miranda Kirby, Jared Sieren...Appendix B: Background InformationIntroductionThis Appendix reports details on how the specific claims were derived at the time of conclusion of literature reviews (8/4/2014) on the use of computed tomography (CT) measures of lung parenchymal density as a method for estimating severity and progression of emphysema in the lungs. Only whole lung measurements are considered. Regional and lobar measures of emphysema are increasingly being investigated and reported in the literature PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5DYXN0YWxkaTwvQXV0aG9yPjxZZWFyPjIwMTM8L1llYXI+

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ADDIN EN.CITE.DATA 20-25. However, the number of studies using regional measures is currently insufficient to assess emphysema severity, likewise their bias and repeatability for studies of emphysema progression. Measurement of whole lung parenchymal lung density with CT has been used for several decades as a clinical research marker of emphysema PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5IYXlodXJzdDwvQXV0aG9yPjxZZWFyPjE5ODQ8L1llYXI+

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ADDIN EN.CITE.DATA 22,26-29, but have not been widely adopted in clinical practice. With the advent of lowreduced dose lung cancer screening CT examinations, now recommended annually by the U.S. Preventative Services Task Force for 55-80 year olds with a 30 or more pack-year history of smoking, the value of serial emphysema CT measurements based on lung density as an important clinical data becomes immediately more relevant ADDIN EN.CITE <EndNote><Cite><Author>Moyer</Author><Year>2014</Year><RecNum>11</RecNum><DisplayText><style face="superscript">30</style></DisplayText><record><rec-number>11</rec-number><foreign-keys><key app="EN" db-id="5s5ee500vesxw9ez5wex59wvssxtpxd9v0av" timestamp="1520460550">11</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Moyer, V. A.</author><author>U. S. Preventive Services Task Force</author></authors></contributors><titles><title>Screening for lung cancer: U.S. Preventive Services Task Force recommendation statement</title><secondary-title>Ann Intern Med</secondary-title></titles><periodical><full-title>Ann Intern Med</full-title></periodical><pages>330-8</pages><volume>160</volume><number>5</number><edition>2014/01/01</edition><keywords><keyword>Aged</keyword><keyword>Aged, 80 and over</keyword><keyword>Cost of Illness</keyword><keyword>Directive Counseling</keyword><keyword>Early Detection of Cancer/adverse effects/*methods</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Lung/diagnostic imaging</keyword><keyword>Lung Neoplasms/*diagnostic imaging/*prevention &amp; control/surgery</keyword><keyword>Male</keyword><keyword>Mass Chest X-Ray/adverse effects</keyword><keyword>Mass Screening/adverse effects/economics/*methods</keyword><keyword>Middle Aged</keyword><keyword>Models, Statistical</keyword><keyword>Risk Assessment</keyword><keyword>Smoking/adverse effects</keyword><keyword>Smoking Cessation</keyword><keyword>Sputum/cytology</keyword><keyword>*Tomography, X-Ray Computed/adverse effects</keyword></keywords><dates><year>2014</year><pub-dates><date>Mar 4</date></pub-dates></dates><isbn>1539-3704 (Electronic)&#xD;0003-4819 (Linking)</isbn><accession-num>24378917</accession-num><urls><related-urls><url>. This will become substantially more important if an effective treatment for emphysema becomes available. If repeat CT examinations were to be performed primarily for emphysema quantification, lowreduced dose techniques are important given the life expectancy of patients this could be applied to and the number of examinations they may undergo. Multiple but related measures of parenchymal density have been applied in clinical research, most prominently the relative area (RA), or low attenuation area (LAA), below specific thresholds of the histogram of lung densities in Hounsfield units (HU). Typically thresholds from -970 through -910 HU are used, at inspiratory lung volume coached to total lung capacity (TLC). The most common thresholds used are RA-950 HU, and RA -910 HU. The RAs are expressed in fractions or percentages. A second related class of measures inverts the relative area of lung below a threshold by specifying a single HU value below which a fixed relative lung area falls. For example, common measures of this class include the HU threshold below which 1% (Perc 1) or 15% (Perc 15Perc15) of the lung area falls, respectively. Unfortunately only a limited number of studies have compared density measurement in the lungs to micro and macroscopic measures of histology derived from similar regions of diseased lung. Measures of tissue histology in the lungs are the gold standard for measuring severity and progression of emphysema but are necessarily limited themselves. Typically, such studies in human subjects or patients depend on obtaining resected tissue after lung surgery PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5IYXlodXJzdDwvQXV0aG9yPjxZZWFyPjE5ODQ8L1llYXI+

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ADDIN EN.CITE.DATA 26-28,31. Consequently, only limited agreement exists as to which of the related CT measures of lung parenchymal density is superior for detecting the presence and severity of emphysema. The more common measures have some empirical consensus based on a combination of comparisons to microscopic histology and associations with known measures of whole lung function. This consensus supports RA-950 HU PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5HZXZlbm9pczwvQXV0aG9yPjxZZWFyPjE5OTY8L1llYXI+

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ADDIN EN.CITE.DATA 27,28,31 and Perc 1 ADDIN EN.CITE <EndNote><Cite><Author>Madani</Author><Year>2006</Year><RecNum>12</RecNum><DisplayText><style face="superscript">31</style></DisplayText><record><rec-number>12</rec-number><foreign-keys><key app="EN" db-id="5s5ee500vesxw9ez5wex59wvssxtpxd9v0av" timestamp="1520460619">12</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Madani, A.</author><author>Zanen, J.</author><author>de Maertelaer, V.</author><author>Gevenois, P. A.</author></authors></contributors><auth-address>Department of Radiology, Hopital Erasme, Universite Libre de Bruxelles, Brussels, Belgium. afarine@ladner-</auth-address><titles><title>Pulmonary emphysema: objective quantification at multi-detector row CT--comparison with macroscopic and microscopic morphometry</title><secondary-title>Radiology</secondary-title></titles><periodical><full-title>Radiology</full-title></periodical><pages>1036-43</pages><volume>238</volume><number>3</number><edition>2006/01/21</edition><keywords><keyword>Adult</keyword><keyword>Aged</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Male</keyword><keyword>Middle Aged</keyword><keyword>Prospective Studies</keyword><keyword>Pulmonary Emphysema/*diagnostic imaging/pathology/surgery</keyword><keyword>Regression Analysis</keyword><keyword>Respiratory Function Tests</keyword><keyword>Statistics, Nonparametric</keyword><keyword>*Tomography, Spiral Computed</keyword></keywords><dates><year>2006</year><pub-dates><date>Mar</date></pub-dates></dates><isbn>0033-8419 (Print)&#xD;0033-8419 (Linking)</isbn><accession-num>16424242</accession-num><urls><related-urls><url> as the measures best correlated to microscopic histology, and Perc 15Perc15 PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TdG9sazwvQXV0aG9yPjxZZWFyPjIwMDc8L1llYXI+PFJl

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ADDIN EN.CITE.DATA 32,33. Therefore, RA-950 HU and Perc 15Perc15, being the most studied and best validated measures in clinical research studies, are hereby recognized as the reference standards and are used to determine the claims for bias and repeatability of lung parenchyma measures in this document. Some general limitations of both RA-950 HU and Perc 15Perc15 should be recognized. RA-950 is especially sensitive to noise, which varies with choice of image reconstruction kernel and mA used for image acquisition. High frequency reconstruction kernels (so-called “hard” algorithms) result in higher absolute RA-950 irrespective of disease severity PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5DaG9uZzwvQXV0aG9yPjxZZWFyPjIwMTI8L1llYXI+PFJl

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ADDIN EN.CITE.DATA 36. This has necessarily focused significant research effort on lung volume adjustment (VA) methods ADDIN EN.CITE <EndNote><Cite><Author>Dirksen</Author><Year>2008</Year><RecNum>7672</RecNum><DisplayText><style face="superscript">37</style></DisplayText><record><rec-number>7672</rec-number><foreign-keys><key app="EN" db-id="rwwtaedax0vw24evew7predtxx5rervs0dad" timestamp="0">7672</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Dirksen, A.</author></authors></contributors><auth-address>Department of Respiratory Medicine, Gentofte University Hospital, Copenhagen, Denmark. adi@dadlnet.dk</auth-address><titles><title>Monitoring the progress of emphysema by repeat computed tomography scans with focus on noise reduction</title><secondary-title>Proc Am Thorac Soc</secondary-title></titles><pages>925-8</pages><volume>5</volume><number>9</number><edition>2008/12/06</edition><keywords><keyword>Algorithms</keyword><keyword>Calibration</keyword><keyword>*Disease Progression</keyword><keyword>Humans</keyword><keyword>Lung/radiography</keyword><keyword>Lung Volume Measurements</keyword><keyword>Pulmonary Disease, Chronic Obstructive/physiopathology/radiography</keyword><keyword>Pulmonary Emphysema/physiopathology/*radiography</keyword><keyword>Research Design</keyword><keyword>*Respiration</keyword><keyword>Software</keyword><keyword>Tomography, X-Ray Computed/*methods</keyword></keywords><dates><year>2008</year><pub-dates><date>Dec 15</date></pub-dates></dates><isbn>1546-3222 (Print)&#xD;1546-3222 (Linking)</isbn><accession-num>19056718</accession-num><urls><related-urls><url> [pii]&#xD;10.1513/pats.200804-033QC</electronic-resource-num><language>eng</language></record></Cite></EndNote>37. Lung VA appears to be justified for longitudinal studies where reduction in the limits of agreement in Bland-Altman analyses is substantial, e.g. on the order of 40% (Table 3), after correction using either statistical regression methods ADDIN EN.CITE <EndNote><Cite><Author>Keller</Author><Year>2011</Year><RecNum>19</RecNum><DisplayText><style face="superscript">38</style></DisplayText><record><rec-number>19</rec-number><foreign-keys><key app="EN" db-id="5s5ee500vesxw9ez5wex59wvssxtpxd9v0av" timestamp="1520461090">19</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Keller, B. M.</author><author>Reeves, A. P.</author><author>Henschke, C. I.</author><author>Yankelevitz, D. F.</author></authors></contributors><auth-address>Department of Radiology, University of Pennsylvania School of Medicine, 3600 Market St, Ste 360, Philadelphia, PA 19104-2643, USA.</auth-address><titles><title>Multivariate compensation of quantitative pulmonary emphysema metric variation from low-dose, whole-lung CT scans</title><secondary-title>AJR Am J Roentgenol</secondary-title></titles><periodical><full-title>AJR Am J Roentgenol</full-title></periodical><pages>W495-502</pages><volume>197</volume><number>3</number><edition>2011/08/25</edition><keywords><keyword>Aged</keyword><keyword>Analysis of Variance</keyword><keyword>Female</keyword><keyword>Fractals</keyword><keyword>Humans</keyword><keyword>Imaging, Three-Dimensional</keyword><keyword>Lung Volume Measurements</keyword><keyword>Male</keyword><keyword>Middle Aged</keyword><keyword>Pulmonary Emphysema/*diagnostic imaging</keyword><keyword>Radiation Dosage</keyword><keyword>Radiographic Image Interpretation, Computer-Assisted</keyword><keyword>Retrospective Studies</keyword><keyword>Severity of Illness Index</keyword><keyword>Tomography, X-Ray Computed/*methods</keyword></keywords><dates><year>2011</year><pub-dates><date>Sep</date></pub-dates></dates><isbn>1546-3141 (Electronic)&#xD;0361-803X (Linking)</isbn><accession-num>21862778</accession-num><urls><related-urls><url>, or the constant lung mass assumption referred to as the “sponge” model PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CYWtrZXI8L0F1dGhvcj48WWVhcj4yMDA1PC9ZZWFyPjxS

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ADDIN EN.CITE.DATA 39,40. It is important to remember that progressive emphysema results in increased in lung volumes, and there is some concern that correction for lung volumes may therefore reduce the apparent increase in emphysema. However, in longitudinal studies, measures of emphysema corrected for lung volume have more consistently demonstrated progression than uncorrected values PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TdG9lbDwvQXV0aG9yPjxZZWFyPjIwMDg8L1llYXI+PFJl

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ADDIN EN.CITE.DATA 43. In Tables 2 and 3 we tabulate the bias and limits of agreement for a subset of longitudinal studies, both before and after performing VA, reported in the above selected studies using linear regression models with fixed and/or random effects. In particular, the sponge model represents the generalized approach to VA applicable to longitudinal studies, i.e. when correction of follow-up lung volume is made relative to a baseline scan. Expected improvements in bias and precision after lung VA are further discussed in Section 3.Study Inclusion and Exclusion Criteria The studies included for estimating the bias and precision of lung parenchymal density with CT had to meet the following inclusion criteria:Publication year of the study after 2005 because CT scanner architectures and stability changed radically when these methods migrated from axial to multi-slice helical platforms and protocols.A minimum of 16 slice detectors were used for CT acquisition to limit maximum breath-hold time to ~10 s with 3D whole lung coverage.The same or similar CT platform was used for repeated scans.The Study methods provided sufficient details regarding CT reconstruction and acquisition parameters to verify consistency.Sufficient data for analysis was included to conduct Bland-Altman analysis to calculate bias and limits of agreement for one or both of RA-950 and Perc 15Perc15. Subjects were scanned twice or more with less than or equal to a 4 month interval between CT scans with the intent of eliminating the influence of possible disease progression on the bias and precision estimates ADDIN EN.CITE <EndNote><Cite><Author>Gietema</Author><Year>2007</Year><RecNum>6604</RecNum><DisplayText><style face="superscript">44</style></DisplayText><record><rec-number>6604</rec-number><foreign-keys><key app="EN" db-id="rwwtaedax0vw24evew7predtxx5rervs0dad" timestamp="0">6604</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Gietema, H. A.</author><author>Schilham, A. M.</author><author>van Ginneken, B.</author><author>van Klaveren, R. J.</author><author>Lammers, J. W.</author><author>Prokop, M.</author></authors></contributors><auth-address>Department of Radiology, University Medical Center, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands. h.gietema@umcutrecht.nl</auth-address><titles><title>Monitoring of smoking-induced emphysema with CT in a lung cancer screening setting: detection of real increase in extent of emphysema</title><secondary-title>Radiology</secondary-title></titles><pages>890-7</pages><volume>244</volume><number>3</number><keywords><keyword>Aged</keyword><keyword>Humans</keyword><keyword>Lung Neoplasms/epidemiology/*radiography</keyword><keyword>Male</keyword><keyword>Mass Screening</keyword><keyword>Middle Aged</keyword><keyword>Netherlands/epidemiology</keyword><keyword>Population Surveillance</keyword><keyword>Pulmonary Emphysema/epidemiology/*etiology/pathology/*radiography</keyword><keyword>Reproducibility of Results</keyword><keyword>Retrospective Studies</keyword><keyword>Smoking/*adverse effects</keyword><keyword>*Tomography, X-Ray Computed</keyword></keywords><dates><year>2007</year><pub-dates><date>Sep</date></pub-dates></dates><accession-num>17709835</accession-num><urls><related-urls><url> </url></related-urls></urls></record></Cite></EndNote>44.Studies were explicitly excluded if:Repeatability data were not included.Parenchymal density was not measured with either RA-950 or Perc 15Perc15.The time interval between repeated CT scans exceeded 4 months or other inclusion criteria listed above were not met.It is inevitable that even for the included studies variability remained, which was mainly in these four areas: CT scanner platform used, Number of subjects, severity of disease, and time interval between CT scans. The studies included are summarized and referenced in Table 1. The density metrics reported in this document are from studies on subjects assumed to be free of disease progression during the short time intervals between baseline and repeat scans. The primary sources of the within-subject variations in the apparent density of the lung parenchyma are attributed primarily to inspiration levels and scanner calibration/measurement error. This approach constrains sources of variation not due to the measurement method. Moreover, at the present time there are few studies that address repeatability with whole lung volume coverage in a reasonable breath-hold (<10 s) even over longer time intervals. There are two major reasons repeatability studies can be excluded: due to scanner architecture PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CYWtrZXI8L0F1dGhvcj48WWVhcj4yMDA1PC9ZZWFyPjxS

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ADDIN EN.CITE.DATA 33,40,49-51. One exception to this is the Park et al study ADDIN EN.CITE <EndNote><Cite><Author>Park</Author><Year>2012</Year><RecNum>21</RecNum><DisplayText><style face="superscript">40</style></DisplayText><record><rec-number>21</rec-number><foreign-keys><key app="EN" db-id="5s5ee500vesxw9ez5wex59wvssxtpxd9v0av" timestamp="1520461180">21</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Park, S. J.</author><author>Lee, C. H.</author><author>Goo, J. M.</author><author>Heo, C. Y.</author><author>Kim, J. H.</author></authors></contributors><auth-address>Interdisciplinary Program in Radiation Applied Life Science, Seoul National University, Seoul, Republic of Korea.</auth-address><titles><title>Inter-scan repeatability of CT-based lung densitometry in the surveillance of emphysema in a lung cancer screening setting</title><secondary-title>Eur J Radiol</secondary-title></titles><periodical><full-title>Eur J Radiol</full-title></periodical><pages>e554-60</pages><volume>81</volume><number>4</number><edition>2011/07/15</edition><keywords><keyword>Absorptiometry, Photon/*methods</keyword><keyword>Adult</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Lung</keyword><keyword>Lung Neoplasms/*complications/*diagnostic imaging</keyword><keyword>Male</keyword><keyword>Mass Screening/*methods</keyword><keyword>Middle Aged</keyword><keyword>Pulmonary Emphysema/*complications/*diagnostic imaging</keyword><keyword>Reproducibility of Results</keyword><keyword>Sensitivity and Specificity</keyword><keyword>Tomography, X-Ray Computed/*methods</keyword></keywords><dates><year>2012</year><pub-dates><date>Apr</date></pub-dates></dates><isbn>1872-7727 (Electronic)&#xD;0720-048X (Linking)</isbn><accession-num>21752566</accession-num><urls><related-urls><url> was included as a reference example in an “asymptomatic population” (GOLD stage 0) with a longer time interval between CT scans (8 months), but the subjects were deemed to have “no perceptual changes of disease progression”.Effect of VA on Bias and Precision Claims for RA-950 and Perc 15Perc15It is generally recognized that VA is useful for improving the precision of repeat CT measurements of lung density PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TaGFrZXI8L0F1dGhvcj48WWVhcj4yMDA0PC9ZZWFyPjxS

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ADDIN EN.CITE.DATA 47. The cross sectional (inter-scanner) variation issue can be addressed by phantom studies ADDIN EN.CITE <EndNote><Cite><Author>Stoel</Author><Year>2004</Year><RecNum>8416</RecNum><DisplayText><style face="superscript">52</style></DisplayText><record><rec-number>8416</rec-number><foreign-keys><key app="EN" db-id="rwwtaedax0vw24evew7predtxx5rervs0dad" timestamp="0">8416</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Stoel, B. C.</author><author>Bakker, M. E.</author><author>Stolk, J.</author><author>Dirksen, A.</author><author>Stockley, R. A.</author><author>Piitulainen, E.</author><author>Russi, E. W.</author><author>Reiber, J. H.</author></authors></contributors><auth-address>Division of Image Processing, Department of Radiology and dagger Department of Pulmonology, Leiden University Medical Center, Leiden, The Netherlands. B.C.Stoel@lumc.nl</auth-address><titles><title>Comparison of the sensitivities of 5 different computed tomography scanners for the assessment of the progression of pulmonary emphysema: a phantom study</title><secondary-title>Invest Radiol</secondary-title><alt-title>Investigative radiology</alt-title></titles><pages>1-7</pages><volume>39</volume><number>1</number><keywords><keyword>Humans</keyword><keyword>Lung/radiography</keyword><keyword>Pulmonary Emphysema/*diagnosis</keyword><keyword>Sensitivity and Specificity</keyword><keyword>*Tomography Scanners, X-Ray Computed</keyword></keywords><dates><year>2004</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>0020-9996 (Print)&#xD;0020-9996 (Linking)</isbn><accession-num>14701982</accession-num><urls><related-urls><url>. In the current document we focus on longitudinal clinical studies from the same site using the same scan protocol, examining the sample mean bias and limits of agreement in the absence of disease progression after VA. Generally the underlying physiological model-- the sponge model in which the lung mass is assumed to be conserved -- affords the simplicity of an inverse proportionality between lung density and volume. However, this model is not strictly followed in clinical data or even in phantom studies ADDIN EN.CITE <EndNote><Cite><Author>Stoel</Author><Year>2014</Year><RecNum>32</RecNum><DisplayText><style face="superscript">53</style></DisplayText><record><rec-number>32</rec-number><foreign-keys><key app="EN" db-id="5s5ee500vesxw9ez5wex59wvssxtpxd9v0av" timestamp="1520461832">32</key></foreign-keys><ref-type name="Personal Communication">26</ref-type><contributors><authors><author>Stoel, B. C.</author></authors></contributors><titles><title>Personal Communication</title></titles><dates><year>2014</year></dates><urls></urls></record></Cite></EndNote>53. The more common approach is a statistical model that assumes a linear combination of effects that contribute to the density variation in repeat scans. The simplest is the linear fixed effect model in which the change in density metrics (dependent variable) is paired with the change in volume (independent variable), and the linear regression analysis returns a slope and intercept which are then used to correct the density metrics in the repeat scan, such that it may now be compared to the baseline scan free of inspiration related variation. The more advanced model adds random effects to address the individual level of variation, recognizing that the fixed effect model only takes into account the variation at the cohort level. Reference PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TdG9lbDwvQXV0aG9yPjxZZWFyPjIwMDg8L1llYXI+PFJl

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ADDIN EN.CITE.DATA 41 has a detailed comparison of different models in the study of clinical data in the context of disease progression. For the studies compiled in this document, the method of VA varies, and is reported without any assessment of statistical or clinical merits. Because more advanced techniques for VA continue to emerge ADDIN EN.CITE <EndNote><Cite><Author>Staring</Author><Year>2014</Year><RecNum>8227</RecNum><DisplayText><style face="superscript">54</style></DisplayText><record><rec-number>8227</rec-number><foreign-keys><key app="EN" db-id="rwwtaedax0vw24evew7predtxx5rervs0dad" timestamp="0">8227</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Staring, M.</author><author>Bakker, M. E.</author><author>Stolk, J.</author><author>Shamonin, D. P.</author><author>Reiber, J. H.</author><author>Stoel, B. C.</author></authors></contributors><auth-address>Department of Radiology, Division of Image Processing, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, The Netherlands.&#xD;Department of Pulmonology, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, The Netherlands.</auth-address><titles><title>Towards local progression estimation of pulmonary emphysema using CT</title><secondary-title>Med Phys</secondary-title><alt-title>Medical physics</alt-title></titles><pages>021905</pages><volume>41</volume><number>2</number><edition>2014/02/11</edition><dates><year>2014</year><pub-dates><date>Feb</date></pub-dates></dates><isbn>0094-2405 (Print)&#xD;0094-2405</isbn><accession-num>24506626</accession-num><urls></urls><electronic-resource-num>10.1118/1.4851535</electronic-resource-num><remote-database-provider>Nlm</remote-database-provider><language>eng</language></record></Cite></EndNote>54, this document does not intend to suggest any particular model or method, but rather provides an analysis of the sample studies available that fit the selection criteria. The results of the VA from the included studies are summarized in Table 3. The data, bias and limits of agreement (LOA) before and after VA are plotted in Figure 1.Mean Repeatability Coefficient Obtained by Meta-AnalysisAn initial assessment ADDIN EN.CITE <EndNote><Cite><Author>Winter</Author><Year>2013</Year><RecNum>35</RecNum><DisplayText><style face="superscript">55</style></DisplayText><record><rec-number>35</rec-number><foreign-keys><key app="EN" db-id="5s5ee500vesxw9ez5wex59wvssxtpxd9v0av" timestamp="1520462332">35</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>Winter, B.</author></authors></contributors><titles><title>Linear models and linear mixed effects models in R with linguistic applications.</title></titles><dates><year>2013</year></dates><publisher>arXiv:1308.5499</publisher><urls></urls></record></Cite></EndNote>55 was performed on a subset of data listed in Table 3 with a fixed effect linear model to test whether VA had an effect on the bias and LOAs of the change in Perc 15Perc15 and RA-950. For Perc 15Perc15, four studies were included, and the mean bias of -0.168 before VA and 0.090 after VA are statistically insignificant (p=0.46). Therefore, it was concluded that VA has negligible effect on the bias. Due to the lack of homogeneity in LOA exhibited by this set of studies, a more rigorous analysis was performed following the QIBA metrology working group guidelines PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5IdWFuZzwvQXV0aG9yPjxZZWFyPjIwMTU8L1llYXI+PFJl

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ADDIN EN.CITE.DATA 58 recommend use of the repeatability coefficient (RC) instead of the LOA for the technical performance assessment of repeatability of a quantitative imaging biomarker (QIB) . Therefore, the RC, which is a function of the within-subject variance, was first obtained based on the reported bias and LOA values for each study, and the meta-analysis was conducted using the random effects model PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5IdWFuZzwvQXV0aG9yPjxZZWFyPjIwMTU8L1llYXI+PFJl

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ADDIN EN.CITE.DATA 56. The results of these analyses are summarized in Table 4 and in the Forest plots in Figure 2. The concept of Smallest Real Difference (SRD) is defined by the RC following the conventions of the published literature for test-retest assessments PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5MZXhlbGw8L0F1dGhvcj48WWVhcj4yMDA1PC9ZZWFyPjxS

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ADDIN EN.CITE.DATA 59-61. More details on the RC and the meta-analysis are in the Appendices of this document. The claim language in Section 5 is separated into without and with VA to reflect a narrower 95% confidence limit as a result of VA. For RA-950, only Ref. PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5DaG9uZzwvQXV0aG9yPjxZZWFyPjIwMTI8L1llYXI+PFJl

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ADDIN EN.CITE.DATA 34 fits the inclusion criteria, and despite making an exception to also allow the Park et al study ADDIN EN.CITE <EndNote><Cite><Author>Park</Author><Year>2012</Year><RecNum>21</RecNum><DisplayText><style face="superscript">40</style></DisplayText><record><rec-number>21</rec-number><foreign-keys><key app="EN" db-id="5s5ee500vesxw9ez5wex59wvssxtpxd9v0av" timestamp="1520461180">21</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Park, S. J.</author><author>Lee, C. H.</author><author>Goo, J. M.</author><author>Heo, C. Y.</author><author>Kim, J. H.</author></authors></contributors><auth-address>Interdisciplinary Program in Radiation Applied Life Science, Seoul National University, Seoul, Republic of Korea.</auth-address><titles><title>Inter-scan repeatability of CT-based lung densitometry in the surveillance of emphysema in a lung cancer screening setting</title><secondary-title>Eur J Radiol</secondary-title></titles><periodical><full-title>Eur J Radiol</full-title></periodical><pages>e554-60</pages><volume>81</volume><number>4</number><edition>2011/07/15</edition><keywords><keyword>Absorptiometry, Photon/*methods</keyword><keyword>Adult</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Lung</keyword><keyword>Lung Neoplasms/*complications/*diagnostic imaging</keyword><keyword>Male</keyword><keyword>Mass Screening/*methods</keyword><keyword>Middle Aged</keyword><keyword>Pulmonary Emphysema/*complications/*diagnostic imaging</keyword><keyword>Reproducibility of Results</keyword><keyword>Sensitivity and Specificity</keyword><keyword>Tomography, X-Ray Computed/*methods</keyword></keywords><dates><year>2012</year><pub-dates><date>Apr</date></pub-dates></dates><isbn>1872-7727 (Electronic)&#xD;0720-048X (Linking)</isbn><accession-num>21752566</accession-num><urls><related-urls><url>, only 2 studies are insufficient to support a meta-analysis to inform the claim for the RA-950 metric. The committee will monitor the literature and make a more definitve claim when using the RA-950 metric in combination with VA when more data becomes available. Claims: For longitudinal studies with lowreduced-dose CT when monitoring patients who have smoking-induced emphysema: Without lung VA, an increase in RA-950 of at least 3.7%, or a decrease in Perc 15Perc15 of at least 18 HU, is required for detection of an increase in the extent of emphysema, with 95% confidence.Without VA, for a measured change of ?x HU in Perc15, one can expect the true change to lie in the interval [?x -18 HU, ?x +18 HU] with 95% confidence; for a measured change of ?y% in RA-950, one can expect the true change to lie in the interval [?y -3.7%, ?y +3.7%] with 95% confidence.With lung VA, a decrease in Perc15 of at least 11 HU, is required for detection of an increase in the extent of emphysema, with 95% confidence.*With VA, from a measured change of ?x HU in Perc 15Perc15, one can expect the true change to lie in the interval [?x -11 HU, ?x +11 HU] with 95% confidence.* No claim is made for the RA-950 measure with volume correction due to the lack of sufficient data at the time of this report.AuthorYearNumber of SubjectsDisease SeverityTime Interval (mean or median)Scanner Manufacturer and ModelDensity MeasuresReconstruction KernelNumber of Centers & Vendor PlatformsTube Current-Time (mAs)Meets Study Inclusion Criteria? (Reason)Park ADDIN EN.CITE <EndNote><Cite><Author>Park</Author><Year>2012</Year><RecNum>21</RecNum><DisplayText><style face="superscript">40</style></DisplayText><record><rec-number>21</rec-number><foreign-keys><key app="EN" db-id="5s5ee500vesxw9ez5wex59wvssxtpxd9v0av" timestamp="1520461180">21</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Park, S. J.</author><author>Lee, C. H.</author><author>Goo, J. M.</author><author>Heo, C. Y.</author><author>Kim, J. H.</author></authors></contributors><auth-address>Interdisciplinary Program in Radiation Applied Life Science, Seoul National University, Seoul, Republic of Korea.</auth-address><titles><title>Inter-scan repeatability of CT-based lung densitometry in the surveillance of emphysema in a lung cancer screening setting</title><secondary-title>Eur J Radiol</secondary-title></titles><periodical><full-title>Eur J Radiol</full-title></periodical><pages>e554-60</pages><volume>81</volume><number>4</number><edition>2011/07/15</edition><keywords><keyword>Absorptiometry, Photon/*methods</keyword><keyword>Adult</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Lung</keyword><keyword>Lung Neoplasms/*complications/*diagnostic imaging</keyword><keyword>Male</keyword><keyword>Mass Screening/*methods</keyword><keyword>Middle Aged</keyword><keyword>Pulmonary Emphysema/*complications/*diagnostic imaging</keyword><keyword>Reproducibility of Results</keyword><keyword>Sensitivity and Specificity</keyword><keyword>Tomography, X-Ray Computed/*methods</keyword></keywords><dates><year>2012</year><pub-dates><date>Apr</date></pub-dates></dates><isbn>1872-7727 (Electronic)&#xD;0720-048X (Linking)</isbn><accession-num>21752566</accession-num><urls><related-urls><url> 08 moSiemens Sensation 16RA-950, Perc 1, Perc 15Perc15B30f1, 140 N (Time Interval)#Chong PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5DaG9uZzwvQXV0aG9yPjxZZWFyPjIwMTI8L1llYXI+PFJl

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ADDIN EN.CITE.DATA 62201199*oNCE or lung cancer >20 pack years3 moSiemens Sensation 16RA-950, Perc 15Perc15“Sharp”; 3X3 Gaussian?1, 130 Y?Gietema ADDIN EN.CITE <EndNote><Cite><Author>Gietema</Author><Year>2007</Year><RecNum>6604</RecNum><DisplayText><style face="superscript">44</style></DisplayText><record><rec-number>6604</rec-number><foreign-keys><key app="EN" db-id="rwwtaedax0vw24evew7predtxx5rervs0dad" timestamp="0">6604</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Gietema, H. A.</author><author>Schilham, A. M.</author><author>van Ginneken, B.</author><author>van Klaveren, R. J.</author><author>Lammers, J. W.</author><author>Prokop, M.</author></authors></contributors><auth-address>Department of Radiology, University Medical Center, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands. h.gietema@umcutrecht.nl</auth-address><titles><title>Monitoring of smoking-induced emphysema with CT in a lung cancer screening setting: detection of real increase in extent of emphysema</title><secondary-title>Radiology</secondary-title></titles><pages>890-7</pages><volume>244</volume><number>3</number><keywords><keyword>Aged</keyword><keyword>Humans</keyword><keyword>Lung Neoplasms/epidemiology/*radiography</keyword><keyword>Male</keyword><keyword>Mass Screening</keyword><keyword>Middle Aged</keyword><keyword>Netherlands/epidemiology</keyword><keyword>Population Surveillance</keyword><keyword>Pulmonary Emphysema/epidemiology/*etiology/pathology/*radiography</keyword><keyword>Reproducibility of Results</keyword><keyword>Retrospective Studies</keyword><keyword>Smoking/*adverse effects</keyword><keyword>*Tomography, X-Ray Computed</keyword></keywords><dates><year>2007</year><pub-dates><date>Sep</date></pub-dates></dates><accession-num>17709835</accession-num><urls><related-urls><url> </url></related-urls></urls></record></Cite></EndNote>442007157“Heavy” Smokers3 moPhillips MX800IDT or Brilliance 16pRA-950B (moderately soft)1, 230 **Y***Subset of a multi-center, multi-platform study for which two baseline scans were obtained.+Subset of subjects with < 4 months between scans*+Subset of 475 subjects screened for cancer*oMILD trial only (16 slice)** 140 kVp (vs. 120 kVp) for > 80kg weight?Smoothing operation in image domain?High frequency instead of smooth kernel used for reconstruction.#Note that Park et al is retained in subsequent analysis in spite of the longer scan intervals, factoring in that the study subjects were an “asymptomatic population” (GOLD stage 0) and the authors’ assertion that the subjects had no perceptual changes of disease progression.Table 1. Summary of parameters for the selected studies used to determine the claims.Table 2. Summary of repeated measures, bias, and LOA for RA-950 and Perc15 for baseline and repeat scans from the selected studies without volume adjustment.AuthorYearLung Volume (ml) RA-950 (%)Perc15 (HU)Mean (SD) Time 1 Mean (SD) Time 2Mean (SD) Time 1 Mean (SD) Time 2Bias (SD) Limits of Agreement Mean (SD) Time 1 Mean (SD) Time 2 Bias (SD)Limits of AgreementPark20124784 (908)4806 (978)8.7 (5.3)9.2 (5.1)0.5 (3.5)-6.4, 7.4-933.4 (13.8)-934.5 (13.7)-1.1 (10.9)-22.1, 20.7Chong20125770 (1540)5724 (NR)16.0 (11.7)NR0.01 (1.17)-2.33, 2.35-946.3 (28.3)NR (NR)0.52 (5.29)-10.07, 11.11Keller 2011NR NR----NRNR-0.39 (10.1)-20.13, 19.35Hochhegger20112578 (584)2518 (591)0.53 (0.77)0.71 (1.19)0.39 (0.88)-1.35, 2.15----Diciotte20116290 (1220)6270(1180) 6.2 (6.7)6.5 (6.8) 0.3 (3.4)#--6.8 , 6.6-925.2 (22.3)-925.3 (21.8)-0.1 (11.5)-20.1, 25.0Gietema20076935 (1267)6945 (1322)0.17 (NR)0.08 (NR)-0.09 (1.19)-1.3, 1.1----NR – “not reported.”For ‘-‘ the measure was not an end-point for the published study.# Diciotte did not report the SD for the RA-950 bias or LOAs; values here are estimated based on the pooled SD of the mean from time 1 and 2, assuming a correlation coefficient being comparable to the one given for the lung volume between time 1 and 2.AuthorMethodRA-950 HU (%)Perc 15Perc15 (HU)Without VAWith VAWithout VAWith VABias (SD) LOABias (SD) LOA ?LOA% ?Bias (SD) LOABias (SD) LOA ?LOA% ?ParkLinear Regression0.5 (3.5)-6.4, 7.41.1 (1.9)-3.6, 3,7-5.247-1.1 (10.9)-22.1, 20.70.3 (6.6)-12.6, 13.2-8.540ChongLinear Regression 0.01 (1.17)-2.33, 2.350.22 (0.83)-1.43, 1.87-0.69290.52 (5.29)-10.7, 11.11-0.42 (1.82)-4.05, 3.21-7.367Keller1Univariate Linear Regression 0.22 (5.19)-9.95, 10.38-0.27 (3.25)-6.63, 6.10-3.837-0.39 (10.07)-20.13, 19.350.48 (6.86)-12.97, 13.94-6.332Multi-variate Linear Regression-0.13 (2.99)-5.98, 5.73-4.3420.41 (5.67)-10.7, 11.52-8.6444HochheggerNo VA------------Diciotte2Linear Regression(log-transformed)-------0.1 (11.5)-20.1, 25.00.0 (8.2) -14.7, 17.3-6.5129GietemaNo VA------------For ‘-‘ the measure was not an end-point for the published study.Shaded cells are not included due to missing data or if for RA-910 HU (as in Keller et al).1Keller et al shows data from RA -910 HU (omitted from Table 2) and Perc 15Perc15 (as reported in Table 2).2linear regression of the change in log-transformed Perc 15Perc15 vs change in log-transformed TLV.Table 3. Comparison of bias and LOA for RA-950 and Perc 15Perc15 for baseline and repeat scans from the selected studies with VA Table 4. Summary of the results of meta-analyses for the repeatability coefficient (RC).Summary table for meta-analysis of RC#studiesSummary Estimate of RC (Weighted Mean) SE 95% CI (2-sided)Lower boundUpper bound Perc 15Perc15 (HU)without VA418.43.05.912.624.3with VA411.43.56.84.618.3RA-950 (%)without VA53.70.71.42.35.1Fig. 1. Bias and limits of agreement compiled from the selected 6 studies: Bias and 95% limits of agreement (hLOA, or half-width of the 95% LOA, defined as 1.96 SDbias) for RA-950 HU (left panel) and Perc 15Perc15 (right panel) both before (blue) and after (red) VA for selected studies (Table 1). The respective hLOAs are plotted as error bars. Study 3 reported RA -910 rather than RA-950. Studies 4, 5, and 6 did not perform VA for RA-950. Studies 4 and 6 did not report Perc 15Perc15 results. Meta-analysis was not performed for RA-950 after VA because there are only two data points (excluding study 3), and one of which (study 2) reported statistically insignificant improvement of precision as a result of VA. For Perc 15Perc15, meta-analysis was performed both before and after VA based on the 4 data points included.Fig 2. Forest plots of the repeatability coefficient (RC) of the studies (blue) and their pooled RC (red), with the 95% CI (defined as 1.96 √SE_RC) shown as error bars: (a) Perc 15Perc15 before VA, (b) Perc 15Perc15 after VA, (c) RA-950 before VA. For RA-950, only two studies performed VA, and the effect of VA on the bias and precision were statistically insignificant.Appendix C: Conventions and Definitions Constructing the repeatability coefficient (RC) based on reported bias and limits of agreementThe studies reported either the standard deviation of the difference (SDbias) or the limits of agreement (LOA) where the half width of LOA (hLOA) is defined as 1.96 SDbias. Conceptually this represents the variability remaining when the between- and within- subjects variability has been accounted for PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5LZXNzbGVyPC9BdXRob3I+PFllYXI+MjAxNTwvWWVhcj48

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ADDIN EN.CITE.DATA 56 1.96 2 wSD. Based on the defintions, the following relationship can be used to calculate the RC given the bias and hLOA:wSD=i12N(Yi1-Yi2)2RC=1.962wSDhLOA=1.961N-1i(Yi1-Yi2)2- NN-1[Y1-Y2 ]2where Y1-Y2=Y1-Y2=bias andY1=iNYi1NY2=iNYi2Nare the means over N subjects for measurements 1 and 2, respectively.Therefore,RC2=N-1 NhLOA2+1.962 bias2Where subscript i = 1 to N represents the subjects, and subscripts 1, 2 represent measurements at time points 1 and 2, respectively. The mean values are taken over the sample size N. When the bias is small, and N is large, RC approaches hLOA. For these studies, the difference is negligible, but the conceptual distinction is asserted here.In addition, an uncertainty has been assigned to the RC itself for each study, based on the gamma distribution with shape and scale parameters defined by the sample size and the RC value itself ADDIN EN.CITE <EndNote><Cite><Author>Winter</Author><Year>2013</Year><RecNum>35</RecNum><DisplayText><style face="superscript">55</style></DisplayText><record><rec-number>35</rec-number><foreign-keys><key app="EN" db-id="5s5ee500vesxw9ez5wex59wvssxtpxd9v0av" timestamp="1520462332">35</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>Winter, B.</author></authors></contributors><titles><title>Linear models and linear mixed effects models in R with linguistic applications.</title></titles><dates><year>2013</year></dates><publisher>arXiv:1308.5499</publisher><urls></urls></record></Cite></EndNote>55. The variance of RC2 is Var(RC2)=kb2, where k =N(P-1)/2 is the shape parameter (P is the number of independent measurements, or studies included in the meta-analysis), and b=RC2/k is the scale parameter. Using the “delta method”, one can show that Var(RC)=Var(RC2)/(4 RC2). Meta-analysis of the performance metric from the four studiesThe goal of the meta-analysis is to derive a mean RC based on the four studies selected. Following ADDIN EN.CITE <EndNote><Cite><Author>Winter</Author><Year>2013</Year><RecNum>35</RecNum><DisplayText><style face="superscript">55</style></DisplayText><record><rec-number>35</rec-number><foreign-keys><key app="EN" db-id="5s5ee500vesxw9ez5wex59wvssxtpxd9v0av" timestamp="1520462332">35</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>Winter, B.</author></authors></contributors><titles><title>Linear models and linear mixed effects models in R with linguistic applications.</title></titles><dates><year>2013</year></dates><publisher>arXiv:1308.5499</publisher><urls></urls></record></Cite></EndNote>55, the Perc 15Perc15 or RA-950 metric data from the four studies were evaluated first using the fixed effects model assuming normal distribution as well as fixed effects model with exact maximum likelihoods. However, the heterogeneity test statistic I2 approaches 100, requiring that the random effects model be used. A variance ? representing the underlying distribution of the RC’s is constructed based on the DerSimonian and Laird estimator for this group of studies ADDIN EN.CITE <EndNote><Cite><Author>Winter</Author><Year>2013</Year><RecNum>35</RecNum><DisplayText><style face="superscript">55</style></DisplayText><record><rec-number>35</rec-number><foreign-keys><key app="EN" db-id="5s5ee500vesxw9ez5wex59wvssxtpxd9v0av" timestamp="1520462332">35</key></foreign-keys><ref-type name="Report">27</ref-type><contributors><authors><author>Winter, B.</author></authors></contributors><titles><title>Linear models and linear mixed effects models in R with linguistic applications.</title></titles><dates><year>2013</year></dates><publisher>arXiv:1308.5499</publisher><urls></urls></record></Cite></EndNote>55, which is added to the variance of each study to modify the weighting factor for the mean RC. The standard error for the mean RC thus obtained is also modified accordingly. This task was performed using Excel (Microsoft, Redmond WA). The same method was also used on the bias of these studies, which were homogeneously small and therefore a simple fixed effect model sufficed. VA does not have any statistically significant effect on the bias.References Cited (Appendices A-C) ADDIN EN.REFLIST 1.Newell JD, Jr., Sieren J, Hoffman EA. Development of quantitative computed tomography lung protocols. J Thorac Imaging. 2013;28(5):266-271.2.Sieren JP, Newell JD, Jr., Barr RG, et al. SPIROMICS Protocol for Multicenter Quantitative Computed Tomography to Phenotype the Lungs. Am J Respir Crit Care Med. 2016;194(7):794-806.3.Dirksen A, Piitulainen E, Parr DG, et al. Exploring the role of CT densitometry: a randomised study of augmentation therapy in alpha1-antitrypsin deficiency. Eur Respir J. 2009;33(6):1345-1353.4.Chapman KR, Burdon JG, Piitulainen E, et al. 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IEEE Trans Med Imaging. 2000;19(9):835-847.9.Zhang J, Bruesewitz MR, Bartholmai BJ, McCollough CH. Selection of appropriate computed tomographic image reconstruction algorithms for a quantitative multicenter trial of diffuse lung disease. J Comput Assist Tomogr. 2008;32(2):233-237.10.Friedman SN, Fung GS, Siewerdsen JH, Tsui BM. A simple approach to measure computed tomography (CT) modulation transfer function (MTF) and noise-power spectrum (NPS) using the American College of Radiology (ACR) accreditation phantom. Med Phys. 2013;40(5):051907.11.Huda W, Sterzik A, Tipnis S, Schoepf UJ. Organ doses to adult patients for chest CT. Med Phys. 2010;37(2):842-847.12.Solomon J, Samei E. Quantum noise properties of CT images with anatomical textured backgrounds across reconstruction algorithms: FBP and SAFIRE. Med Phys. 2014;41(9):091908.13.Hu S, Hoffman EA, Reinhardt JM. Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. 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How to assess the reliability of measurements in rehabilitation. Am J Phys Med Rehabil. 2005;84(9):719-723.60.Weir JP. Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. J Strength Cond Res. 2005;19(1):231-240.61.Tesio L. Outcome measurement in behavioural sciences: a view on how to shift attention from means to individuals and why. Int J Rehabil Res. 2012;35(1):1-12.62.Diciotti S, Sverzellati N, Kauczor HU, et al. Defining the intra-subject variability of whole-lung CT densitometry in two lung cancer screening trials. Acad Radiol. 2011;18(11):1403-1411.Appendix D: Model-specific Instructions and Parameters For acquisition modalities, reconstruction software and software analysis tools, profile conformance requires meeting the activity specifications above in Sections 2, 3 and 4. Sample acquisition and reconstruction protocols that are designed around the requirements of this Profile are provided for several common scanner makes and models in the Excel file “CT Protocols QIBA” linked at: <QIBA Wiki link here>. Just using these parameters without meeting the requirements specified in the profile is not sufficient to achieve conformance. Conversely, it is possible to use different compatible parameters and still achieve conformance. Sites using models listed here are encouraged to consider using these parameters for both simplicity and consistency. Sites using models not listed here may be able to devise their own settings that result in data meeting the requirements.IMPORTANT: The presence of a product model/version in these tables does not imply it has demonstrated conformance with the QIBA Profile. Refer to the specifications and procedures in Sections 3 and 4, and the QIBA Conformance Statement for the product if available. Appendix E: Image Analysis Software ComparisonThis Appendix provides analysis software parameters that are expected to be compatible with the profile requirements. Research, open source and commercial vendors were invited to participant anonymously. The Radiological Society of North America (RSNA) acted as a neutral broker between vendors and the QIBA Lung Density committee to ensure the committee was blinded to vendor identify. All vendors indicated if their software was for research use/open-source or commercial. Vendors were instructed to generate measurements: 1) without segmentation quality assurance (QA) and manual correction to evaluate inter-software reproducibility; 2) repeated on the same images to evaluate intra-software reproducibility; and, 3) repeated on the same images with segmentation QA and manual correction. The reproducibility coefficient (RDC) (ref) was generated to measure the reproducibility across the different vendors for each measurement at standard dose: 1) with and without QA, and 2) by vendor type (research/open-source, commercial). The RDC is the value under which the difference between repeated measurements on the same patient acquired under different conditions should fall within 95% probability, and is defined as:(1)where 2 is the mean of the variances of repeated measurements on the same patient. Low RDC values indicate high reproducibility between vendors. 95% confidence intervals for the RDC were constructed using bootstrapping with 5000 resamples.A total of 8 vendors participated in the software comparison study; n=4 research/open-source and n=4 commercial. A single commercial vendor withdrew from the study. Table 1 shows the reproducibility coefficient (RDC) for TLV, LAA-950RA-950 and Perc15 for 8 different software vendors with and without quality assurance (QA) using manual correction of the lung volume segmentation. Overall, inter-software RDC was low at 0.35L, 1.2% and 1.8HU for TLV, LAA-950RA-950 and Perc15, respectively. For all vendors, inter-software RDC remained unchanged following QA: 0.35L, 1.2% and 1.8HU for TLV, LAA-950RA-950 and Perc15, respectively. Intra-software RDC was also generated by having the vendors perform repeated measurements without QA; all vendors had an intra-software RDC of 0. Table 2 shows the RDC for TLV, RA-950 and Perc15 by vendor type (research/open source or commercial) without and with QA. Research and commercial vendors RDC was comparable for TLV, RA-950 and Perc15 measurements: 0.39L / 0.32L, 1.2% / 1.2%, and 1.7HU / 1.6 HU, respectively. As shown in Table 2, QA had minimal impact on measurement reproducibility between vendors.Table 1. The RDC for TLV, LAA950 and Perc15 for All Vendors at Standard Dose with and without QAInter-software RDCWithout QAInter-software RDC With QAParameterRDC95% CIRDC95% CITLV (L)Total0.350.32 – 0.370.350.32 – 0.37Vendor 10.380.35 – 0.410.380.35 – 0.42Vendor 20.260.24 – 0.270.260.24 – 0.28Vendor 30.260.24 – 0.290.260.24 – 0.29Vendor 40.480.46 – 0.510.480.45 – 0.51Vendor 50.250.23 – 0.270.250.23 – 0.27Vendor 60.460.43 – 0.490.460.43 – 0.49Vendor 70.310.28 – 0.340.310.28 – 0.34Vendor 90.260.24 – 0.280.260.24 – 0.28LAA950 (%)Total1.21.0 – 1.41.21.0 – 1.4Vendor 11.21.0 – 1.51.21.0 – 1.5Vendor 21.10.9 – 1.21.10.9 – 1.2Vendor 31.10.9 – 1.21.10.9 – 1.2Vendor 41.20.9 – 1.41.20.9 – 1.4Vendor 51.21.0 – 1.31.21.0 – 1.3Vendor 61.51.2 – 1.81.51.2 – 1.8Vendor 70.90.7 – 1.00.90.7 – 1.0Vendor 91.21.0 – 1.41.21.0 – 1.4Perc15 (HU)Total1.81.6 – 2.01.81.6 – 2.1Vendor 11.61.4 – 1.91.71.4 – 1.9Vendor 21.51.3 – 1.71.61.3 – 1.8Vendor 31.51.3 – 1.61.51.3 – 1.6Vendor 42.32.1 – 2.62.32.1 – 2.6Vendor 52.11.9 – 2.32.11.9 – 2.3Vendor 62.01.6 – 2.32.01.6 – 2.4Vendor 71.41.2 – 1.71.41.2 – 1.6Vendor 91.71.5 – 1.91.71.5 – 1.9Table 2 shows the RDC for TLV, LAA-950 and Perc15 by vendor type (research/open source or commercial) without and with QA. Research and commercial vendors RDC was comparable for TLV, LAA-950 and Perc15 measurements: 0.39L / 0.32L, 1.2% / 1.2%, and 1.7HU / 1.6 HU, respectively. As shown in Table 2, QA had minimal impact on measurement reproducibility between vendors.Table 2. The RDC for TLV, LAA950 and Perc15 for Research/Open-source and Commercial Vendors for Standard DoseParameterInter-software RDC without QA95% CIInter-software RDC with QA95% CITLV (L)Research/Open Source0.390.36 – 0.410.390.36 – 0.41Commercial0.320.29 – 0.340.320.29 – 0.35LAA950 (%)Research/Open Source1.20.9 – 1.41.20.9 – 1.4Commercial1.21.0 – 1.31.11.0 – 1.3Perc15 (HU)Research/Open Source1.71.5 – 1.91.71.5 – 1.9Commercial1.61.3 – 1.91.61.3 – 2.0Table 2 shows the RDC for TLV, LAA-950 and Perc15 by vendor type (research/open source or commercial) without and with QA. Research and commercial vendors RDC was comparable for TLV, LAA-950 and Perc15 measurements: 0.39L / 0.32L, 1.2% / 1.2%, and 1.7HU / 1.6 HU, respectively. As shown in Table 2, QA had minimal impact on measurement reproducibility between vendors.Table 2. The RDC for TLV, LAA950 and Perc15 for Research/Open-source and Commercial Vendors for Standard DoseParameterInter-software RDC without QA95% CIInter-software RDC with QA95% CITLV (L)Research/Open Source0.390.36 – 0.410.390.36 – 0.41Commercial0.320.29 – 0.340.320.29 – 0.35LAA950 (%)Research/Open Source1.20.9 – 1.41.20.9 – 1.4Commercial1.21.0 – 1.31.11.0 – 1.3Perc15 (HU)Research/Open Source1.71.5 – 1.91.71.5 – 1.9Commercial1.61.3 – 1.91.61.3 – 2.0 ................
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