University of Liverpool
Measurement of blood-brain barrier permeability using dynamic contrast-enhanced magnetic resonance imaging with reduced scan timeJonghyun Bae*,1,2,3, Jin Zhang2,3, Youssef Zaim Wadghiri2,3, Atul Singh Minhas4, Harish Poptani4, Yulin Ge2,3, Sungheon Gene Kim2,3Affiliations 1. Sackler Institute of Graduate Biomedical Science, New York University School of Medicine2. Bernard and Irene Schwartz Center for Biomedical Imaging, Radiology, New York University School of Medicine3. Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine4. Centre for Preclinical Imaging, Institute of Translational Medicine, University of Liverpool, UK* Corresponding AuthorAddress all correspondence to:Jonghyun Bae, Department of Radiology, NYU School of Medicine, 660 First Avenue, 4th FloorNew York, NY 10016, Email: Jonghyun.Bae@. Tel: +1-212-263-2717Word Count: 4466Running title: BBB permeability using DCE-MRIAbstract Purpose: To investigate the feasibility of measuring the subtle disruption of blood-brain barrier (BBB) using dynamic contrast-enhanced(DCE) MRI with a scan duration shorter than 10 min.Methods: The extended Patlak-model(EPM) was introduced to include the effect of plasma flow (Fp) in the estimation of vascular permeability-surface area product (PS). Numerical simulation studies were carried out to investigate how the reduction in scan time affects the accuracy in estimating contrast kinetic parameters. DCE-MRI studies of the rat brain were conducted with Fisher rats to confirm the results from the simulation. Intracranial F98 glioblastoma models were used to assess areas with different levels of permeability. In the normal brain tissues, the Patlak-model(PM) and EPM were compared, while the two-compartment-exchange-model (TCM) and EPM were assessed in the peri-tumor and the tumor regions. Results: The simulation study results demonstrated that scan time reduction could lead to larger bias in PS estimated by PM (> 2000%) than by EPM (< 47%), especially when Fp is low. When Fp was high as in the gray matter, the bias in PM-PS (>900%) were larger than that in EPM-PS (<42%). The animal study also showed similar results, where the PM parameters were more sensitive to the scan duration than the EPM parameters. It was also demonstrated that, in the peri-tumor region, the EPM parameters showed less change by scan duration than the TCM parameters. Conclusion: The results of this study suggest that EPM can be used to measure PS with a scan duration of 10 min or less. INTRODUCTION Dynamic contrast enhanced (DCE)-MRI has been suggested as an effective tool for non-invasive measurement of blood-brain barrier (BBB) disruption in many brain disorders such as brain tumor PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TaW5naDwvQXV0aG9yPjxZZWFyPjIwMDc8L1llYXI+PFJl
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ADDIN EN.CITE.DATA (6), CSF examination is invasive and provides only indirect measures of BBB breakdown. Hence, there is clearly an unmet need for non-invasive and direct biomarkers which can be used in a clinical setting with a relatively shorter scan time for early detection of BBB disruption in AD and other neurological diseases with vascular permeability change.Previous studies found that the permeability of gray matter BBB is 0.5-2.0x10-3 min-1 PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5IZXllPC9BdXRob3I+PFllYXI+MjAxNjwvWWVhcj48UmVj
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ADDIN EN.CITE.DATA (4,7) which is about two-to-three orders-of-magnitude smaller than the vascular permeability observed in tumor vessels. Because of such low permeability of the BBB, DCE-MRI studies are often conducted with a low temporal resolution (20s – 1 min/frame) and long scan duration (20-35 min) ADDIN EN.CITE <EndNote><Cite><Author>Heye</Author><Year>2014</Year><RecNum>58</RecNum><DisplayText>(8)</DisplayText><record><rec-number>58</rec-number><foreign-keys><key app="EN" db-id="zew99tda8s5zxretadq5a2vtw0e0ae2aps9e" timestamp="1507472188">58</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Heye, A. K.</author><author>Culling, R. D.</author><author>Valdes Hernandez Mdel, C.</author><author>Thrippleton, M. J.</author><author>Wardlaw, J. M.</author></authors></contributors><auth-address>Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK.
College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK.</auth-address><titles><title>Assessment of blood-brain barrier disruption using dynamic contrast-enhanced MRI. A systematic review</title><secondary-title>Neuroimage Clin</secondary-title><alt-title>NeuroImage. Clinical</alt-title></titles><periodical><full-title>Neuroimage Clin</full-title><abbr-1>NeuroImage. Clinical</abbr-1></periodical><alt-periodical><full-title>Neuroimage Clin</full-title><abbr-1>NeuroImage. Clinical</abbr-1></alt-periodical><pages>262-74</pages><volume>6</volume><keywords><keyword>Aged</keyword><keyword>Animals</keyword><keyword>Blood-Brain Barrier/*pathology</keyword><keyword>Contrast Media</keyword><keyword>Humans</keyword><keyword>Magnetic Resonance Imaging/*methods</keyword><keyword>Middle Aged</keyword></keywords><dates><year>2014</year></dates><isbn>2213-1582 (Electronic)
2213-1582 (Linking)</isbn><accession-num>25379439</accession-num><urls><related-urls><url>;(8). Conventional DCE-MRI methods have an inherent trade-off between temporal resolution and spatial resolution, usually resulting in a low temporal resolution. Jelescu et al utilized a dual temporal resolution protocol to improve the accuracy of BBB permeability estimation PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5KZWxlc2N1PC9BdXRob3I+PFllYXI+MjAxMTwvWWVhcj48
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ADDIN EN.CITE.DATA (11) suggesting that it requires a scan time of longer than 15 min for accurate measurement of BBB permeability using the Patlak model. In brain tissue with small leakage, the Patlak model ADDIN EN.CITE <EndNote><Cite><Author>Patlak</Author><Year>1983</Year><RecNum>103</RecNum><DisplayText>(12)</DisplayText><record><rec-number>103</rec-number><foreign-keys><key app="EN" db-id="zew99tda8s5zxretadq5a2vtw0e0ae2aps9e" timestamp="1507482187">103</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Patlak, C. S.</author><author>Blasberg, R. G.</author><author>Fenstermacher, J. D.</author></authors></contributors><titles><title>Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data</title><secondary-title>J Cereb Blood Flow Metab</secondary-title><alt-title>Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism</alt-title></titles><periodical><full-title>J Cereb Blood Flow Metab</full-title></periodical><pages>1-7</pages><volume>3</volume><number>1</number><edition>1983/03/01</edition><keywords><keyword>*Blood-Brain Barrier</keyword><keyword>Humans</keyword><keyword>*Models, Biological</keyword><keyword>Time Factors</keyword></keywords><dates><year>1983</year><pub-dates><date>Mar</date></pub-dates></dates><isbn>0271-678X (Print)
0271-678x</isbn><accession-num>6822610</accession-num><urls></urls><electronic-resource-num>10.1038/jcbfm.1983.1</electronic-resource-num><remote-database-provider>NLM</remote-database-provider><language>eng</language></record></Cite></EndNote>(12) assumes that the reverse flow of the contrast agent from the interstitial space to the vascular space is negligible during the scan time. In addition, the Patlak model uses another important assumption regarding the arterial input function (AIF); the contrast agent concentration in the capillary bed is same as that of the artery. This assumption does not hold true when the flow is not high enough to guarantee that the AIF measured in a large artery represents the contrast concentration curve in the capillary bed. This assumption about the vascular concentration may become more important to consider when the scan time is reduced. While previous studies demonstrated the limited performance of the conventional Patlak with the reduced scan-time, there is paucity of studies on what type of contrast kinetic models should be used in order to reduce the scan time for measurement of low permeability and limited flow.Hence, the purpose of this study is to assess the feasibility of reducing the scan time of DCE-MRI while maintaining the accurate detection of the BBB permeability change. We investigated the role of contrast kinetic model in measurement of low permeability values and what needs to be considered for data acquisition in a shorter scan time. This study was conducted using numerical simulations and DCE-MRI study of the rat brain.METHODSContrast Kinetic Models for BBB PermeabilityIn DCE-MRI data analysis, one of widely used parameters is volume transfer rate constant, commonly known as Ktrans PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Ub2Z0czwvQXV0aG9yPjxZZWFyPjE5OTk8L1llYXI+PFJl
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ADDIN EN.CITE.DATA (13). This parameter determines how fast contrast agents move from the vascular space to extravascular space depending on the vascular endothelial permeability-surface area product (PS) and plasma perfusion (Fp), i.e., Ktrans = Fp (1-exp(-PS/Fp)) PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5CdW9uYWNjb3JzaTwvQXV0aG9yPjxZZWFyPjIwMDc8L1ll
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ADDIN EN.CITE.DATA (7,14). Tissue microcirculation environment including both PS and Fp is often modeled as a two-compartment exchange model (TCM) (Fig. 1a). Gadolinium-based contrast agent (GBCA) is introduced to the capillary compartment with the plasma volume fraction of vp at the rate of Fp. Then the bidirectional exchange of the GBCA between the capillary compartment and extravascular extracellular space (EES) is determined by the rate constant PS and the difference between the blood plasma concentration and EES concentration of the GBCA. The concentration of GBCA of the tissue (Ct) is expressed as the following using the TCM ADDIN EN.CITE <EndNote><Cite><Author>Sourbron</Author><Year>2012</Year><RecNum>97</RecNum><DisplayText>(15)</DisplayText><record><rec-number>97</rec-number><foreign-keys><key app="EN" db-id="zew99tda8s5zxretadq5a2vtw0e0ae2aps9e" timestamp="1507482187">97</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Sourbron, S. P.</author><author>Buckley, D. L.</author></authors></contributors><auth-address>Division of Medical Physics, University of Leeds, Leeds, West Yorkshire, UK.</auth-address><titles><title>Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability</title><secondary-title>Phys Med Biol</secondary-title><alt-title>Physics in medicine and biology</alt-title></titles><periodical><full-title>Phys Med Biol</full-title><abbr-1>Physics in medicine and biology</abbr-1></periodical><alt-periodical><full-title>Phys Med Biol</full-title><abbr-1>Physics in medicine and biology</abbr-1></alt-periodical><pages>R1-33</pages><volume>57</volume><number>2</number><edition>2011/12/17</edition><keywords><keyword>*Blood Circulation</keyword><keyword>*Capillary Permeability</keyword><keyword>Humans</keyword><keyword>Kinetics</keyword><keyword>Magnetic Resonance Imaging/*methods</keyword><keyword>*Models, Biological</keyword></keywords><dates><year>2012</year><pub-dates><date>Jan 21</date></pub-dates></dates><isbn>0031-9155</isbn><accession-num>22173205</accession-num><urls><related-urls><url>;(15):Ctt=vpCpt+veCet[1]vpCp't=FpCat+PSCet-Fp+PSCpt[2]veCe't=PSCpt-PSCet[3]where ve is the extravascular-extracellular volume fraction, Ca GBCA concentration in the artery, Ce GBCA concentration in extravascular-extracellular compartment and Cp GBCA concentration in the capillary compartment.The brain tissue with low permeability has been modeled using a simplified model, known as the Patlak model (PM) ADDIN EN.CITE <EndNote><Cite><Author>Patlak</Author><Year>1983</Year><RecNum>103</RecNum><DisplayText>(12)</DisplayText><record><rec-number>103</rec-number><foreign-keys><key app="EN" db-id="zew99tda8s5zxretadq5a2vtw0e0ae2aps9e" timestamp="1507482187">103</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Patlak, C. S.</author><author>Blasberg, R. G.</author><author>Fenstermacher, J. D.</author></authors></contributors><titles><title>Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data</title><secondary-title>J Cereb Blood Flow Metab</secondary-title><alt-title>Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism</alt-title></titles><periodical><full-title>J Cereb Blood Flow Metab</full-title></periodical><pages>1-7</pages><volume>3</volume><number>1</number><edition>1983/03/01</edition><keywords><keyword>*Blood-Brain Barrier</keyword><keyword>Humans</keyword><keyword>*Models, Biological</keyword><keyword>Time Factors</keyword></keywords><dates><year>1983</year><pub-dates><date>Mar</date></pub-dates></dates><isbn>0271-678X (Print)
0271-678x</isbn><accession-num>6822610</accession-num><urls></urls><electronic-resource-num>10.1038/jcbfm.1983.1</electronic-resource-num><remote-database-provider>NLM</remote-database-provider><language>eng</language></record></Cite></EndNote>(12), as depicted in Fig.1b. The PM assumes that the vascular compartment has infinite flow from the feeding artery and the extravasated GBCA does not return to the vascular compartment within the scan time. The PM can be expressed as:Ctt=vpCat+Cet,[4]Ce't=PSCpt.[5]The PM has been widely used as its linearity allows simple and fast analysis via a graphical method ADDIN EN.CITE <EndNote><Cite><Author>Patlak</Author><Year>1983</Year><RecNum>103</RecNum><DisplayText>(12)</DisplayText><record><rec-number>103</rec-number><foreign-keys><key app="EN" db-id="zew99tda8s5zxretadq5a2vtw0e0ae2aps9e" timestamp="1507482187">103</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Patlak, C. S.</author><author>Blasberg, R. G.</author><author>Fenstermacher, J. D.</author></authors></contributors><titles><title>Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data</title><secondary-title>J Cereb Blood Flow Metab</secondary-title><alt-title>Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism</alt-title></titles><periodical><full-title>J Cereb Blood Flow Metab</full-title></periodical><pages>1-7</pages><volume>3</volume><number>1</number><edition>1983/03/01</edition><keywords><keyword>*Blood-Brain Barrier</keyword><keyword>Humans</keyword><keyword>*Models, Biological</keyword><keyword>Time Factors</keyword></keywords><dates><year>1983</year><pub-dates><date>Mar</date></pub-dates></dates><isbn>0271-678X (Print)
0271-678x</isbn><accession-num>6822610</accession-num><urls></urls><electronic-resource-num>10.1038/jcbfm.1983.1</electronic-resource-num><remote-database-provider>NLM</remote-database-provider><language>eng</language></record></Cite></EndNote>(12) where the linear regression can be used to estimate PS and vp., which is often referred to as a graphical approach of the Patlak model (GPM). In this study, PM represents a non-linear fit of Eq.[4-5] to the entire time course of the data, whereas GPM is for a linearized approach of Eq.[4-5] using only the tail part (after 3-min post-injection) of the kinetic data. The effectiveness of the PM and GPM has been well established for the brain tissues. However, when the scan time is not long enough, it becomes problematic to apply PM or GPM as the contribution of the vascular compartment needs to be measured more accurately PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Xb25nPC9BdXRob3I+PFllYXI+MjAxNzwvWWVhcj48UmVj
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ADDIN EN.CITE.DATA (9-11).The PM can be extended to include a more accurate estimation of vascular effect and not to depend on the assumption that the plasma flow is high enough to ignore the transfer between the feeding artery and the capillary bed, thus referred to as Extended Patlak model (EPM). A similar concept was used for the Tissue Uptake Model in previous studies ADDIN EN.CITE <EndNote><Cite><Author>Kallehauge</Author><Year>2017</Year><RecNum>61</RecNum><DisplayText>(16)</DisplayText><record><rec-number>61</rec-number><foreign-keys><key app="EN" db-id="zew99tda8s5zxretadq5a2vtw0e0ae2aps9e" timestamp="1507472188">61</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Kallehauge, J. F.</author><author>Sourbron, S.</author><author>Irving, B.</author><author>Tanderup, K.</author><author>Schnabel, J. A.</author><author>Chappell, M. A.</author></authors></contributors><auth-address>Institute of Biomedical Engineering, Department of Engineering Science University of Oxford, Oxford, United Kingdom.
Division of Biomedical Imaging, University of Leeds, Leeds, United Kingdom.
Department of Oncology, Aarhus University Hospital, Aarhus, Denmark.
Division of Imaging Science and Biomedical Engineering, King's College London, London, United Kingdom.</auth-address><titles><title>Comparison of linear and nonlinear implementation of the compartmental tissue uptake model for dynamic contrast-enhanced MRI</title><secondary-title>Magn Reson Med</secondary-title><alt-title>Magnetic resonance in medicine</alt-title></titles><periodical><full-title>Magn Reson Med</full-title><abbr-1>Magnetic resonance in medicine</abbr-1></periodical><alt-periodical><full-title>Magn Reson Med</full-title><abbr-1>Magnetic resonance in medicine</abbr-1></alt-periodical><pages>2414-2423</pages><volume>77</volume><number>6</number><dates><year>2017</year><pub-dates><date>Jun</date></pub-dates></dates><isbn>1522-2594 (Electronic)
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ADDIN EN.CITE.DATA (17) was used for the input function (Fig.2a). The concentration curve Ct(t) generated using the TCM was converted to the longitudinal relaxation rate R1 which is defined, with the assumption of fast exchange limit ADDIN EN.CITE <EndNote><Cite><Author>Kim</Author><Year>2007</Year><RecNum>109</RecNum><DisplayText>(18)</DisplayText><record><rec-number>109</rec-number><foreign-keys><key app="EN" db-id="zew99tda8s5zxretadq5a2vtw0e0ae2aps9e" timestamp="1507647755">109</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Kim, S.</author><author>Quon, H.</author><author>Loevner, L. A.</author><author>Rosen, M. A.</author><author>Dougherty, L.</author><author>Kilger, A. M.</author><author>Glickson, J. D.</author><author>Poptani, H.</author></authors></contributors><auth-address>Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA. Sungheon.Kim@uphs.upenn.edu</auth-address><titles><title>Transcytolemmal water exchange in pharmacokinetic analysis of dynamic contrast-enhanced MRI data in squamous cell carcinoma of the head and neck</title><secondary-title>J Magn Reson Imaging</secondary-title></titles><periodical><full-title>J Magn Reson Imaging</full-title><abbr-1>Journal of magnetic resonance imaging : JMRI</abbr-1></periodical><pages>1607-17</pages><volume>26</volume><number>6</number><keywords><keyword>Bayes Theorem</keyword><keyword>Body Water/*metabolism</keyword><keyword>Carcinoma, Squamous Cell/*metabolism</keyword><keyword>Chi-Square Distribution</keyword><keyword>Contrast Media/*pharmacokinetics</keyword><keyword>Gadolinium DTPA/*pharmacokinetics</keyword><keyword>Head and Neck Neoplasms/*metabolism</keyword><keyword>Humans</keyword><keyword>Lymphatic Metastasis</keyword><keyword>Magnetic Resonance Imaging/*methods</keyword><keyword>Movement</keyword></keywords><dates><year>2007</year><pub-dates><date>Dec</date></pub-dates></dates><isbn>1053-1807 (Print)
1053-1807 (Linking)</isbn><accession-num>17968962</accession-num><urls><related-urls><url>;(18), as:R1t= Ctt* r10+ 1T10 [9]where r10 is the relaxivity of the gadolinium chelate, and T10 is the T1 of the tissue before the contrast injection. r10 and T10 were fixed to 4.3 mM-1s-1 and 1.5 s, respectively, in this study. Then, the signal time-intensity curve S(t) was calculated using the signal equation of spoiled gradient echo sequence ADDIN EN.CITE <EndNote><Cite><Author>Tofts</Author><Year>1995</Year><RecNum>110</RecNum><DisplayText>(19)</DisplayText><record><rec-number>110</rec-number><foreign-keys><key app="EN" db-id="zew99tda8s5zxretadq5a2vtw0e0ae2aps9e" timestamp="1507648112">110</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Tofts, P. S.</author><author>Berkowitz, B.</author><author>Schnall, M. D.</author></authors></contributors><auth-address>Institute of Neurology, National Hospital for Neurology and Neurosurgery, London, United Kingdom.</auth-address><titles><title>Quantitative analysis of dynamic Gd-DTPA enhancement in breast tumors using a permeability model</title><secondary-title>Magn Reson Med</secondary-title></titles><periodical><full-title>Magn Reson Med</full-title><abbr-1>Magnetic resonance in medicine</abbr-1></periodical><pages>564-8</pages><volume>33</volume><number>4</number><keywords><keyword>Breast/*pathology</keyword><keyword>Breast Neoplasms/*diagnosis</keyword><keyword>Capillary Permeability</keyword><keyword>*Contrast Media</keyword><keyword>Female</keyword><keyword>Gadolinium DTPA</keyword><keyword>Humans</keyword><keyword>Image Enhancement/methods</keyword><keyword>Magnetic Resonance Imaging/*methods</keyword><keyword>*Organometallic Compounds</keyword><keyword>Pentetic Acid/*analogs & derivatives</keyword><keyword>Signal Processing, Computer-Assisted</keyword></keywords><dates><year>1995</year><pub-dates><date>Apr</date></pub-dates></dates><isbn>0740-3194 (Print)
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ADDIN EN.CITE.DATA (20) of the rat brain to serve as the reference to our animal study.A simulation study was also conducted to investigate the range of PS adequate for the PM and EPM. For this study, PS was varied from 10-4 to 10-2 min-1, while other parameters were kept constant. This simulation study was then repeated for the case when Fp is high as in the gray matter and low as in the white matter, respectively (Table 1). Animal ModelWe used the intracranial F98 glioblastoma model that can provide a wide range of vascular permeability ranging from that of the intact BBB and to highly leaky vessels of the tumor. Three syngeneic female Fisher rats (4-6 weeks old, 120-150 g) were used. A 5 uL suspension of 50,000 F98 cells in phosphate-buffered saline was injected into the cortex at a depth of 3 mm with a Hamilton syringe and a 30-gauge needle using stereotactic apparatus (3mm lateral and 3mm posterior to the bregma). For the imaging study, the animal was mounted on a cradle after anesthesia had been induced with 3% isoflurane in oxygen. The rat head was secured in a nose cone and a restraining device with ear pins to minimize motion-artifacts. During the scan, anesthesia was maintained with 1.5 % isoflurane in oxygen. The animal body temperature was maintained at 37±1 ?C during the scan by directing a thermostatically controlled circulating water blanket over the animal. The animal imaging study was conducted at the University of Liverpool. Data AcquisitionAll MR studies were conducted on a 9.4 T scanner (Bruker BioSpin MRI, Ettlingen, Germany). A three-dimensional spoiled gradient echo sequence was employed for dynamic scans with dual echoes to correct for the T2* effect PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5aaGFuZzwvQXV0aG9yPjxZZWFyPjIwMTU8L1llYXI+PFJl
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ADDIN EN.CITE.DATA (21). Then the T2* corrected time-intensity curves were converted to the concentration curves using Eq.9 and 10. T10 of tissue was assumed to be 1.8 s ADDIN EN.CITE <EndNote><Cite><Author>van de Ven</Author><Year>2007</Year><RecNum>106</RecNum><DisplayText>(22)</DisplayText><record><rec-number>106</rec-number><foreign-keys><key app="EN" db-id="zew99tda8s5zxretadq5a2vtw0e0ae2aps9e" timestamp="1507561727">106</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>van de Ven, R. C.</author><author>Hogers, B.</author><author>van den Maagdenberg, A. M.</author><author>de Groot, H. J.</author><author>Ferrari, M. D.</author><author>Frants, R. R.</author><author>Poelmann, R. E.</author><author>van der Weerd, L.</author><author>Kiihne, S. R.</author></authors></contributors><auth-address>Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.</auth-address><titles><title>T(1) relaxation in in vivo mouse brain at ultra-high field</title><secondary-title>Magn Reson Med</secondary-title><alt-title>Magnetic resonance in medicine</alt-title></titles><periodical><full-title>Magn Reson Med</full-title><abbr-1>Magnetic resonance in medicine</abbr-1></periodical><alt-periodical><full-title>Magn Reson Med</full-title><abbr-1>Magnetic resonance in medicine</abbr-1></alt-periodical><pages>390-5</pages><volume>58</volume><number>2</number><edition>2007/07/27</edition><keywords><keyword>Analysis of Variance</keyword><keyword>Animals</keyword><keyword>Brain Mapping/*methods</keyword><keyword>Contrast Media</keyword><keyword>Female</keyword><keyword>Gadolinium DTPA</keyword><keyword>Magnetic Resonance Imaging/*methods</keyword><keyword>Mice</keyword><keyword>Mice, Inbred C57BL</keyword><keyword>Phantoms, Imaging</keyword></keywords><dates><year>2007</year><pub-dates><date>Aug</date></pub-dates></dates><isbn>0740-3194 (Print)
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ADDIN EN.CITE.DATA (17) was used for the input function. The baseline of AIF was manually adjusted to align with the contrast agent injection time for each rat. The effect of scan time was investigated by using only the initial portion for the data for the scan time of interest, such as 5, 8, 13, and 16 min. A Mann-Whitney U test was used to compare contrast kinetic parameters estimated by different kinetic models with the Bonferroni multiple comparison correction. All statistical tests were conducted at the two-sided 5% significance level using Matlab (MathWorks, MA).RESULTSEffect of scan time on PS estimationThe results from the numerical simulation study are summarized in Figure 4. The PS values estimated from the PM and EPM are shown in Fig.4c. In all cases considered in the study, the PS values estimated using the PM are significantly different from the true values (p < 0.007). In contrast, the PS estimation using EPM is not significantly different from the true value for scan-time less than 13 min, when the flow is high or when the flow is low but the true PS value is high (p>0.007) (Figure 4c, column 1-3). Furthermore, the bias with PM-PS increases near-exponentially as the scan time decreases. When the flow is high and the scan-time of 5 min is used, PM-PS has more than 900% bias, while that from the EPM is around 42%. The bias with the PM-PS increases substantially more when the flow is low. The PM-PS had extremely large bias (up to 3270%) when the scan-time is 5 min. Even with the longest scan-time, the PM-PS has more than 200% bias. The EPM-PS maintains the bias < 47% for the scan times less than 10 min and the bias < 83% for the scan times longer than 10 min. The GPM-PS demonstrated better accuracy than the PM-PS (Supporting Figure S1). However, the GPM suffers from the lack of precision, especially when the scan time is reduced. The scan time dependency of PS is reduced when Fp is high, for instance Fp=0.9 min-1 as in the rat brain (Supporting Figure S3d). However, the PM-PS still exhibits the overestimation as the scan time decreases, although noticeably smaller compared to the low flow case.Estimation of high PS values Fig.5 shows the estimated PS from the simulated data sets with different levels of PS between 10-4 and 10-2 min-1. When the scan time is 15 min long, both models produce relatively accurate estimation up to PS level of 0.003 min-1, but they under-estimate the higher PS values. The bias with high PS (> 0.003 min-1) becomes larger (up to 47%) as the scan-time increases to 30 min. When the scan time is 5 min, the EPM estimates PS with less than 8.5% bias in high PS values (>0.001 min-1) and shows similar trends for both high and low flow. In contrast, PM-PS shows a larger degree of overestimation (bias up to 308 %) when the flow is low. GPM-PS showed underestimation up to 47% in high PS values (>0.001 min-1) when the scan time is 5 min (Supporting Figure S2). GPM-PS has a larger deviation from the true value than PM-PS or EPM-PS when the scan time is 15 min or 30 min.Effect of scan time on vp estimationFig.4a shows the vp values estimated by using the PM or EPM with different scan durations. Overall, the PM underestimates vp compared to the estimation from the EPM estimates and the true values. When the flow is high (Fp = 0.58 mm-1) and the scan time is longer than 10 min, the PM vp is significantly lower (p<0.007) than the true value (bias ≤ 9%). The bias in the PM vp estimates increased to about 12.3% when the scan-time under 10 min is used. When the flow is low (Fp = 0.12 mm-1), for the same scan-time, the PM vp is substantially lower (p < 0.007) than the true value. Even with the scan time of 30 min, the bias is larger than 30%. As the scan-time decreases, the bias increases to 47.5%. In contrast, for the scan time of less than 10 min, the EPM vp has about 0.8% bias when the flow is low (Fp = 0.12 mm-1), and bias less than 0.5% when the flow is high (Fp = 0.58 mm-1). For the scan-time of 30 min, EPM vp has slightly increased bias (<1.5%), but still substantially lower than that of PM vp.Effect of scan time on Fp estimationFig.4b shows the flow estimates from the EPM. Note that the PM does not provide an estimate of Fp. For both cases of low and high Fp, the estimation bias remained less than 1.4% over the range of scan times assessed in this study and the estimated Fp values did not significantly differ from the truth value (p>0.01).Normal contralateral rat brainThe results of the animal study are summarized in Fig.6. Fig.6a shows the vp estimated using both the PM and EPM for each rat. The PM-vp estimates are lower than the EPM-vp for all scan times. The PM-vp are underestimated as the scan-time is reduced, which shares the similar trend as the simulation. The EPM-vp estimates also exhibit the similar result as the simulation, and those are close to the literature value shown in Table 1. The flow estimated using the EPM is shown in Fig.6b. For all scan times, the estimated flow value does not change noticeably.The estimated PS values from the two models are shown in Fig.6c. In all cases with three rats and scan times, the PM-PS estimates are significantly higher than the EPM-PS (p < 0.001). As the scan time decreases, the PM-PS is overestimated compared to the estimates using the full data, while the EPM-PS remains relatively constant regardless of the reduced scan time. These patterns are similar to the trend observed in the simulation result. Tumor lesions in rat brainFig.7a shows the estimated PS values in the region with the intermediate permeability for different scan times. For all cases with three rats, EPM-PS estimates show a slight underestimation but relatively consistent estimation for the scan-time of 8min or longer, as opposed to TCM-PS estimates with the reduced scan-time overestimates the value as compared to the estimates using the full data.The PS estimates in the regions with the high permeability are shown in Fig.7b. EPM-PS estimates, regardless of the scan times, were significantly underestimated as compared to TCM-PS estimates. The interquartile ranges of the TCM-PS estimates were substantially larger than those of the EPM-PS estimates.DISCUSSIONIn this study, we demonstrated that the EPM could be used to reduce scan time without compromising the accuracy and precision of PS and vp estimation, in comparison with the PM, while providing accurate estimation of Fp that is not available from the PM. There are several studies that measured the vascular permeability in the brain using DCE-MRI. 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ADDIN EN.CITE.DATA (4,5,7). These studies typically used the PM for the kinetic analysis, and there is lack of studies in which the accuracy of PM is investigated when scan time of 10 min or shorter is used. The results in the present study suggest that the integrity of BBB can be assessed using a DCE-MRI protocol with a clinically feasible scan time in the order of about 10 min when an appropriate contrast kinetic model, such as the EPM, is used for data analysis. One of the key underlying assumptions of the PM is that the concentration of the capillary compartment is same as that in the artery where the AIF is measured ADDIN EN.CITE <EndNote><Cite><Author>Patlak</Author><Year>1983</Year><RecNum>103</RecNum><DisplayText>(12)</DisplayText><record><rec-number>103</rec-number><foreign-keys><key app="EN" db-id="zew99tda8s5zxretadq5a2vtw0e0ae2aps9e" timestamp="1507482187">103</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Patlak, C. S.</author><author>Blasberg, R. G.</author><author>Fenstermacher, J. D.</author></authors></contributors><titles><title>Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data</title><secondary-title>J Cereb Blood Flow Metab</secondary-title><alt-title>Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism</alt-title></titles><periodical><full-title>J Cereb Blood Flow Metab</full-title></periodical><pages>1-7</pages><volume>3</volume><number>1</number><edition>1983/03/01</edition><keywords><keyword>*Blood-Brain Barrier</keyword><keyword>Humans</keyword><keyword>*Models, Biological</keyword><keyword>Time Factors</keyword></keywords><dates><year>1983</year><pub-dates><date>Mar</date></pub-dates></dates><isbn>0271-678X (Print)
0271-678x</isbn><accession-num>6822610</accession-num><urls></urls><electronic-resource-num>10.1038/jcbfm.1983.1</electronic-resource-num><remote-database-provider>NLM</remote-database-provider><language>eng</language></record></Cite></EndNote>(12). This assumption is valid if Fp is sufficiently high or the contrast agent concentration does not change fast as in the later portion of DCE-MRI data. In DCE-MRI data with a long scan time, the PM can be a valid contrast kinetic model since it is able to describe the later portion of the dynamic data where the contrast concentration in the capillary is close to that in the artery. However, it is not the case when the scan time becomes as short as 10 min or less. The actual contrast agent concentration in the capillary bed with low blood flow can be quite different from that is assumed by the PM (Supporting Figure S4). In this study, we found that the effect of scan time could be noticeable by the increased bias in the PM-estimated parameters when the flow was 0.58 min-1, the level observed in the grey matter ADDIN EN.CITE <EndNote><Cite><Author>Barbier</Author><Year>2001</Year><RecNum>104</RecNum><DisplayText>(23)</DisplayText><record><rec-number>104</rec-number><foreign-keys><key app="EN" db-id="zew99tda8s5zxretadq5a2vtw0e0ae2aps9e" timestamp="1507496256">104</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Barbier, E. L.</author><author>Lamalle, L.</author><author>Decorps, M.</author></authors></contributors><auth-address>Laboratoire mixte INSERM U438, Universite Joseph Fourier: RMN Bioclinique, LRC-CEA, Hopital Albert Michallon, Grenoble, France.</auth-address><titles><title>Methodology of brain perfusion imaging</title><secondary-title>J Magn Reson Imaging</secondary-title><alt-title>Journal of magnetic resonance imaging : JMRI</alt-title></titles><periodical><full-title>J Magn Reson Imaging</full-title><abbr-1>Journal of magnetic resonance imaging : JMRI</abbr-1></periodical><alt-periodical><full-title>J Magn Reson Imaging</full-title><abbr-1>Journal of magnetic resonance imaging : JMRI</abbr-1></alt-periodical><pages>496-520</pages><volume>13</volume><number>4</number><edition>2001/03/29</edition><keywords><keyword>Animals</keyword><keyword>*Cerebrovascular Circulation</keyword><keyword>Contrast Media</keyword><keyword>Humans</keyword><keyword>Magnetic Resonance Imaging/*methods</keyword><keyword>Models, Theoretical</keyword><keyword>Spin Labels</keyword></keywords><dates><year>2001</year><pub-dates><date>Apr</date></pub-dates></dates><isbn>1053-1807 (Print)
1053-1807</isbn><accession-num>11276094</accession-num><urls></urls><remote-database-provider>NLM</remote-database-provider><language>eng</language></record></Cite></EndNote>(23). When the flow was as low as 0.12 min-1 as in the white matter PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5MYXJzc29uPC9BdXRob3I+PFllYXI+MjAxNzwvWWVhcj48
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ADDIN EN.CITE.DATA (25). The study found that subjects with elevated vascular risk burden experienced the reduced CBF and aging was also significantly associated with the reduced cortical CBF. The association of aging with reduced CBF was also found in other studies PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5BbWluLUhhbmphbmk8L0F1dGhvcj48WWVhcj4yMDE1PC9Z
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ADDIN EN.CITE.DATA (26). These studies suggest that CBF can serve as a promising biomarker for the pathologic stages of AD. Therefore, the PM may not be an adequate model to study AD, as it cannot provide any information about the CBF and also likely to have larger bias in the estimated kinetic parameters due to the reduced CBF in aging population. In contrast, the proposed EPM can measure Fp which aids the PS estimation, and could serve as a biomarker for the vascular change in AD and other neurological diseases. The results in this study indicate that the proposed model EPM is not suitable for all range of PS values. As demonstrated by our simulation (Fig.5) and tumor analysis (Fig.7), when PS surpasses certain levels (around 0.003 min-1), the contrast agent exchange between the compartments is no longer unidirectional; there is a non-negligible amount of reverse flow coming back from the EES within the scan time. This invalidates the underlying assumption of both EPM and PM, and clearly leads to underestimation of PS values. In case of peri-tumor data shown in Fig.7, the EPM-PS is smaller than the PS estimated by TCM, but it appears that both EPM-PS and TCM-PS have similar range of data distribution, suggesting that EPM-PS may capture the data variability estimated by TCM-PS. It is interesting to note that, when the scan time is reduced, the distribution of EPM-PS remains at a similar level while the distribution of TCM-PS is substantially shifted to a higher level with a noticeably wide range, suggesting that the uncertainty of TCM-PS increases when the scan time is not long enough. In case of tumor ROI, the distribution of EPM-PS is much narrower than that of TCM-PS, regardless of scan time, which indicates that EPM-PS cannot estimate high PS values. The current study presents data to support that the EPM can be an appropriate model to measure the PS with a reduced scan time. However, there are some limitations of the study. It is noted that peri-tumoral regions were used as the brain regions with increased BBB leakiness where we did not have any control on the degree of permeability change. Future studies can be conducted with a means to open BBB, such as focused ultrasound of a region of the brain in conjunction with micro-bubbles PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5WbGFjaG9zPC9BdXRob3I+PFllYXI+MjAxMTwvWWVhcj48
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ADDIN EN.CITE.DATA (27). This method will be helpful to design a study with various degrees of permeability change in a local area. The proposed method also needs to be tested in subjects with normal aging versus neurological diseases. In conclusion, our study demonstrated that the proposed EPM can provide accurate estimation of PS over the range where the permeability of tissue has changed from subtle to intermediate levels, approximately up to 0.007 min-1, when a short scan time is used. Most importantly, our results substantiate that the EPM can be used with a reduced scan time of less than 10 min. Another advantage of the EPM is that it can measure blood flow and corrects for a possible effect of flow changes in PS estimation. Future studies are warranted to investigate the feasibility of using the proposed EPM in clinical settings to assess BBB integrity in cerebral small vessel diseases PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5BbWluLUhhbmphbmk8L0F1dGhvcj48WWVhcj4yMDE1PC9Z
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ADDIN EN.CITE.DATA (26).ACKNOWLEDGEMENTSThis work was supported in part by an NIH grant R01CA160620 and Alzheimer’s Association Research Grant AARG-17-533484. The Center for Advanced Imaging Innovation and Research (CAI2R, ) at New York University School of Medicine is supported by NIH/NIBIB P41 EB017183. In vivo rat imaging data were obtained in the Centre for Preclinical Imaging (CPI) of the University of Liverpool, which has been funded by a Medical Research Council (MRC) grant (MR/L012707/1). We would like to acknowledge Dr. Authr Taylor, CPI, University of Liverpool for maintaining the F98 cell line and for the help with the in vivo tumor model.TABLE and FIGURE LEGENDSTable 1. Contrast kinetic parameter values used for simulation studies: volume fraction of extracellular-extravascular space(EES), ve; volume fraction of the blood plasma compartment, vp; the blood flow from the artery to the capillary bed, Fp; and the bidirectional endothelial permeability-surface-product, PS.Figure 1. Contrast kinetic models considered in this study. (a) Two-compartment exchange model (TCM). (b) Patlak model (PM), (c) Extended Patlak model (EPM).Figure 2. Examples of time-intensity curves used in the simulation study. (a) Population based arterial input function(AIF). (b) An example of tissue time-concentration curve with ve=0.2, vp=0.02, Fp=1.0 min-1, and PS=0.001 min-1.Figure 3. Representative post-contrast images of a rat brain with tumor. (a) T1 weighted image. (b) An ROI (ref) selected for the normal appearing brain parenchyma in the contralateral side.Figure 4. Summary of the numerical simulation study to assess the effect of scan time on contrast kinetic parameters. The simulation study was conducted with four different conditions of true Fp and PS as shown in Table 1. Each column of box-whisker plots presents the result for one of four conditions: H/H, High Fp/High PS; H/L, High Fp /Low PS; L/H, Low Fp /High PS; L/L, Low Fp /Low PS. Each row shows one of contrast kinetic parameters; (a) vp, (b) Fp, (c) PS, (d) PS with a reduced range of y-axis.Figure 5. Box-whisker plots of the PS values estimated using the PM (blue triangles) and EPM (red boxes) when the true PS values are between 10-4 and 10-2 min-1. This simulation study is conducted for two conditions: the true flow(Fp) being high and low. (a) Results with high true Fp, (b) Results with low true FpFigure 6. Contrast kinetic parameters, vp (a), Fp (b), and PS (c), estimated from normal appearing brain parenchyma of three rat brains are shown using box-whisker plots. Figure 7. Box- whisker plots of PS estimated from the peri-tumor region (a) and tumor region (b).Supporting Figure S1. Summary of the numerical simulation study to assess the effect of scan time on contrast kinetic parameters estimated using the EPM and GPM (Graphical Patlak model) analyses. The simulation study was conducted with four different conditions of true Fp and PS as shown in Table 1. Each column of box-whisker plots presents the result for one of four conditions: H/H, High Fp/High PS; H/L, High Fp /Low PS; L/H, Low Fp /High PS; L/L, Low Fp /Low PS. Each row shows one of contrast kinetic parameters; (a) vp and (b) PS (c) PS with a reduced range of y-axis..Supporting Figure S2. Box-whisker plots of the PS values estimated using the GPM (magenta) and EPM (red) when the true PS values are between 10-4 and 10-2 min-1. This simulation study is conducted for two conditions: the true flow(Fp) being high and low. (a) Results with high true Fp, (b) Results with low true FpSupporting Figure S3. Summary of the numerical simulation study to assess the effect of scan time on contrast kinetic parameters. The simulation study was conducted with two different conditions of true PS as shown in Table 1 and the high Fp = 0.9 min-1, simulating the blood flow of a rat brain. Each row shows one of contrast kinetic parameters; (a) vp, (b) vp with a reduced range of y-axis, (c) Fp, (d) PSSupporting Figure S4. Simulated concentration-time curve in the plasma compartment (Cp). These curves are generated with the same vp = 0.02, and PS = 1.25 × 10-4 min-1, but with different Fp = 0.58 and 0.121 min-1 for EPM. The Cp from the PM is displayed in red, and the Cp from the EPM with two different blood flows are displayed in green and blue.REFERENCES ADDIN EN.REFLIST 1.Singh A, Haris M, Rathore D, Purwar A, Sarma M, Bayu G, Husain N, Rathore RK, Gupta RK. Quantification of physiological and hemodynamic indices using T(1) dynamic contrast-enhanced MRI in intracranial mass lesions. Journal of magnetic resonance imaging : JMRI 2007;26(4):871-880.2.Kassner A, Roberts TP, Moran B, Silver FL, Mikulis DJ. 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Contrast kinetic parameter values used for simulation studies: volume fraction of extracellular-extravascular space(EES), ve; volume fraction of the blood plasma compartment, vp; the blood flow from the artery to the capillary bed, Fp; and the bidirectional endothelial permeability-surface-product, PS.Fp (min-1)PS (×10-4min-1)vevpHigh0.58(Grey matter ADDIN EN.CITE <EndNote><Cite><Author>Barbier</Author><Year>2001</Year><RecNum>104</RecNum><DisplayText>(23)</DisplayText><record><rec-number>104</rec-number><foreign-keys><key app="EN" db-id="zew99tda8s5zxretadq5a2vtw0e0ae2aps9e" timestamp="1507496256">104</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Barbier, E. L.</author><author>Lamalle, L.</author><author>Decorps, M.</author></authors></contributors><auth-address>Laboratoire mixte INSERM U438, Universite Joseph Fourier: RMN Bioclinique, LRC-CEA, Hopital Albert Michallon, Grenoble, France.</auth-address><titles><title>Methodology of brain perfusion imaging</title><secondary-title>J Magn Reson Imaging</secondary-title><alt-title>Journal of magnetic resonance imaging : JMRI</alt-title></titles><periodical><full-title>J Magn Reson Imaging</full-title><abbr-1>Journal of magnetic resonance imaging : JMRI</abbr-1></periodical><alt-periodical><full-title>J Magn Reson Imaging</full-title><abbr-1>Journal of magnetic resonance imaging : JMRI</abbr-1></alt-periodical><pages>496-520</pages><volume>13</volume><number>4</number><edition>2001/03/29</edition><keywords><keyword>Animals</keyword><keyword>*Cerebrovascular Circulation</keyword><keyword>Contrast Media</keyword><keyword>Humans</keyword><keyword>Magnetic Resonance Imaging/*methods</keyword><keyword>Models, Theoretical</keyword><keyword>Spin Labels</keyword></keywords><dates><year>2001</year><pub-dates><date>Apr</date></pub-dates></dates><isbn>1053-1807 (Print)
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ADDIN EN.CITE.DATA (5))(a) (b) (c) Figure 1. Contrast kinetic models considered in this study. (a) Two-compartment exchange model (TCM). (b) Patlak model (PM), (c) Extended Patlak model (EPM).Figure 2. Examples of time-intensity curves used in the simulation study. (a) Population based arterial input function(AIF). (b) An example of tissue time-concentration curve with ve=0.2, vp=0.02, Fp=1.0 min-1, and PS=0.001 min-1.Figure 3. Representative post-contrast images of a rat brain with tumor. (a) T1 weighted image. (b) An ROI (ref) selected for the normal appearing brain parenchyma in the contralateral side.Figure 4. Summary of the numerical simulation study to assess the effect of scan time on contrast kinetic parameters. The simulation study was conducted with four different conditions of true Fp and PS as shown in Table 1. Each column of box-whisker plots presents the result for one of four conditions: H/H, High Fp/High PS; H/L, High Fp /Low PS; L/H, Low Fp /High PS; L/L, Low Fp /Low PS. Each row shows one of contrast kinetic parameters; (a) vp, (b) Fp, (c) PS, (d) PS with a reduced range of y-axis.Figure 5. Box-whisker plots of the PS values estimated using the PM (blue triangles) and EPM (red boxes) when the true PS values are between 10-4 and 10-2 min-1. This simulation study is conducted for two conditions: the true flow(Fp) being high and low. (a) Results with high true Fp, (b) Results with low true FpFigure 6. Contrast kinetic parameters, vp (a), Fp (b), and PS (c), estimated from normal appearing brain parenchyma of three rat brains are shown using box-whisker plots. Figure 7. Box-whisker plots of PS estimated from the peri-tumor region (a) and tumor region (b). ................
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