University of Edinburgh
Reproducibility and variability of quantitative MRI markers in cerebral small vessel diseaseFran?ois De Guio1,2, Eric Jouvent1,2,3, Geert Jan Biessels4, Sandra E. Black5, Carol Brayne6, Christopher Chen7, Charlotte Cordonnier8, Frank-Eric De Leeuw9, Martin Dichgans10,11, Fergus Doubal12, Marco Duering10, Carole Dufouil13, Emrah Duzel14, Franz Fazekas15, Vladimir Hachinski16, M. Arfan Ikram17,18, Jennifer Linn19, Paul M. Matthews20, Bernard Mazoyer21, Vincent Mok22, Bo Norrving23, John T. O’Brien24, Leonardo Pantoni25, Stefan Ropele15, Perminder Sachdev26, Reinhold Schmidt15, Sudha Seshadri27, Eric E. Smith28, Luciano A. Sposato16, Blossom Stephan29, Richard H. Swartz5, Christophe Tzourio13, Mark van Buchem30, Aad van der Lugt17, Robert van Oostenbrugge31, Meike W. Vernooij17, Anand Viswanathan32, David Werring33, Frank Wollenweber10, Joanna M. Wardlaw12,34, Hugues Chabriat1,2,31University Paris Diderot, Sorbonne Paris Cité, UMRS 1161 INSERM, Paris, France; 2DHU NeuroVasc Sorbonne Paris Cité, Paris, France; 3AP-HP, Lariboisière Hosp, Department of Neurology, Paris, France; 4Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands?; 5Department of Medicine (Neurology), Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada?; 6Department of Public Health and Primary Care, Cambridge University, Cambridge, UK?; 7Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore?; 8University of Lille, CHU Lille, Inserm U1171, Lille, France?; 9Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Department of Neurology, Nijmegen, The Netherlands?; 10Institute for Stroke and Dementia Research, Klinikum der Universit?t München, Ludwig-Maximilian-University (LMU), Munich, Germany?; 11Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; 12Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK?; 13Inserm UMR897, University of Bordeaux, Bordeaux, France?; 14Department of Cognitive Neurology and Dementia Research, University of Magdeburg, Magdeburg, Germany?; 15Department of Neurology, Medical University of Graz, Graz, Austria?; 16Department of Clinical Neurological Sciences, University of Western Ontario, London, Canada?; 17Department of Radiology and department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands?; 18Department of Neurology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands?; 19Department of Neuroradiology, University Hospital Munich, Munich, Germany?; 20Division of Brain Sciences, Department of Medicine, Imperial College London, London, UK?; 21CNRS-CEA UMR5296, University of Bordeaux, Bordeaux, France?; 22Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China?; 23Department of Clinical Sciences, Neurology, Lund University, Lund, Sweden; 24Department of Psychiatry, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK?; 25NEUROFARBA Department, University of Florence, Florence, Italy?; 26Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia; 27Department of Neurology, Boston University School of Medicine, Boston, USA?; 28Department of Clinical Neurosciences and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada; 29Institute of Health and Society, Newcastle University Institute of Ageing, Newcastle University, Newcastle, UK?; 30Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands?; 31Department of Neurology, Maastricht University Medical Center, Maastricht, The Netherlands?; 32Department of Neurology, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, USA; 33Stroke Research Group, Department of Brain Repair and Rehabilitation, UCL, London, UK?; 34Centre for Cognitive Ageing and Cognitive Epidemiology (CCACE), University of Edinburgh, Edinburgh, UKCorresponding author: Pr Hugues Chabriat, Dept of Neurology, Hopital Lariboisière, 2 rue Ambroise Paré, 75010 Paris, France. Email: hugues.chabriat@aphp.fr This study was supported by grants from Fondation Avenir and Fondation Leducq (Transatlantic Network of Excellence on the Pathogenesis of Small Vessel Disease of the Brain, ).This work follows from a project funded by the Centres of Excellence in Neurodegeneration (COEN), ref MRC MR/J006971/1, “Standards for determining the vascular contribution to neurodegeneration” in which many of the authors participated.Word countAbstract: 170Full text: 7893Figures: 4Table: 1Abstract (200 words)Brain imaging is essential for the diagnosis and characterization of cerebral small vessel disease (SVD). Several magnetic resonance imaging (MRI) markers have therefore emerged, providing new information on the diagnosis, progression and mechanisms of SVD. Yet, the reproducibility of these SVD markers has received little attention despite being widely used in cross-sectional and longitudinal studies. This review focuses on the main SVD-related markers on MRI including: white matter hyperintensities, lacunes, dilated perivascular spaces, microbleeds and brain volume. The aim is to summarise, for each marker, what is currently known about: 1) its reproducibility in studies with a scan-rescan procedure either in single or multi centre settings; 2) the acquisition-related sources of variability; and, 3) the techniques used to minimise this variability. Based on the results, we discuss technical and other challenges that need to be overcome in order for these markers to be reliably used as outcome measures in future clinical trials. We also highlight the key points that need to be considered when designing multicentre MRI studies of SVD.Keywords: MRI, cerebral small vessel disease, marker, brain volume, atrophy, white matter hyperintensities, microbleeds, lacunes, perivascular spaces, reproducibility, repeatability, variabilityIntroductionCerebral small vessel disease (SVD) is a major source of cognitive impairment and is the second most common cause of dementia in older aged people. ADDIN EN.CITE <EndNote><Cite><Author>Román</Author><Year>2002</Year><RecNum>174</RecNum><DisplayText><style face="superscript">1</style></DisplayText><record><rec-number>174</rec-number><foreign-keys><key app="EN" db-id="0w0d5w0vt0areaeafstvvw01vaa9s5ww2r2e">174</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Román, Gustavo C</author><author>Erkinjuntti, Timo</author><author>Wallin, Anders</author><author>Pantoni, Leonardo</author><author>Chui, Helena C</author></authors></contributors><auth-address>University of Texas at San Antonio and the Audie L Murphy Memorial Veterans Hospital, San Antonio, Texas 78284-7883, USA. romang@uthscsa.edu</auth-address><titles><title>Subcortical ischaemic vascular dementia</title><secondary-title>Lancet Neurology</secondary-title></titles><periodical><full-title>Lancet neurology</full-title></periodical><pages>426-436</pages><volume>1</volume><number>7</number><dates><year>2002</year><pub-dates><date>Nov 01</date></pub-dates></dates><urls><pdf-urls><url> SVD is typically sporadic and related to aging and vascular risk factors, but can also be caused by genetic conditions ADDIN EN.CITE <EndNote><Cite><Author>Chabriat</Author><Year>2009</Year><RecNum>1016</RecNum><DisplayText><style face="superscript">2</style></DisplayText><record><rec-number>1016</rec-number><foreign-keys><key app="EN" db-id="0w0d5w0vt0areaeafstvvw01vaa9s5ww2r2e">1016</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Chabriat, Hugues</author><author>Joutel, Anne</author><author>Dichgans, Martin</author><author>Tournier-Lasserve, Elizabeth</author><author>Bousser, Marie-Germaine</author></authors></contributors><auth-address>Service de Neurologie, Groupe Hospitalier Lariboisière-Fernand-Widal, Assistance Publique H?pitaux de Paris, F-75010, Paris, France.</auth-address><titles><title>Cadasil</title><secondary-title>Lancet Neurology</secondary-title></titles><periodical><full-title>Lancet neurology</full-title></periodical><pages>643-653</pages><volume>8</volume><number>7</number><dates><year>2009</year><pub-dates><date>Jul 01</date></pub-dates></dates><urls><related-urls><url>(09)70127-9</electronic-resource-num><language>eng</language></record></Cite></EndNote>2. The most frequent sporadic forms of SVD are arteriolosclerosis and cerebral amyloid angiopathy (CAA). A significant proportion of patients with SVD have associated neurodegenerative diseases such as Alzheimer’s Disease (AD), which can lead to a mixed dementia. ADDIN EN.CITE <EndNote><Cite><Author>Selnes</Author><Year>2006</Year><RecNum>87</RecNum><DisplayText><style face="superscript">3</style></DisplayText><record><rec-number>87</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">87</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Selnes, O. A.</author><author>Vinters, H. V.</author></authors></contributors><titles><title>Vascular cognitive impairment</title><secondary-title>Nature Clinical Practice Neurology</secondary-title></titles><periodical><full-title>Nature Clinical Practice Neurology</full-title></periodical><pages>538-547</pages><volume>2</volume><number>10</number><dates><year>2006</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>1745-834X</isbn><accession-num>WOS:000240619000009</accession-num><urls><related-urls><url><Go to ISI>://WOS:000240619000009</url></related-urls></urls><electronic-resource-num>10.1038/ncpneuro0294</electronic-resource-num></record></Cite></EndNote>3 In therapeutic trials for dementias, surrogate imaging markers of both pathophysiological processes should ideally be used to account for the potential mixed origin.Pathological features of SVD are heterogeneous and out of reach of current in vivo imaging techniques. Nonetheless, parenchymal lesions presumed to be caused by small vessel changes have been adopted as markers of SVD, giving neuroimaging a central role for diagnosis and characterization ADDIN EN.CITE <EndNote><Cite><Author>Pantoni</Author><Year>2010</Year><RecNum>467</RecNum><DisplayText><style face="superscript">4</style></DisplayText><record><rec-number>467</rec-number><foreign-keys><key app="EN" db-id="aapftssat9zp0aepatvp9x090ar5d9925wwd" timestamp="1400830397">467</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Pantoni, L.</author></authors></contributors><auth-address>Department of Neurological and Psychiatric Sciences, University of Florence, Italy. pantoni@unifi.it</auth-address><titles><title>Cerebral small vessel disease: from pathogenesis and clinical characteristics to therapeutic challenges</title><secondary-title>Lancet Neurol</secondary-title><alt-title>Lancet neurology</alt-title></titles><periodical><full-title>Lancet Neurol</full-title><abbr-1>Lancet neurology</abbr-1></periodical><alt-periodical><full-title>Lancet Neurol</full-title><abbr-1>Lancet neurology</abbr-1></alt-periodical><pages>689-701</pages><volume>9</volume><number>7</number><edition>2010/07/09</edition><keywords><keyword>Age Factors</keyword><keyword>Anticoagulants/therapeutic use</keyword><keyword>Blood Vessels/*pathology</keyword><keyword>Cerebrovascular Circulation/physiology</keyword><keyword>Cerebrovascular Disorders/classification/*etiology/*pathology/*therapy</keyword><keyword>Cognition Disorders/physiopathology</keyword><keyword>Diagnostic Imaging/methods</keyword><keyword>Endarterectomy, Carotid/methods</keyword><keyword>Humans</keyword><keyword>Thrombolytic Therapy/methods</keyword></keywords><dates><year>2010</year><pub-dates><date>Jul</date></pub-dates></dates><isbn>1474-4465 (Electronic)
1474-4422 (Linking)</isbn><accession-num>20610345</accession-num><work-type>Review</work-type><urls><related-urls><url>(10)70104-6</electronic-resource-num></record></Cite></EndNote>4. The spectrum of brain magnetic resonance imaging (MRI) manifestations of SVD is wide, ranging from minor white matter hyperintensities (WMH) often seen in population-based studies to numerous subcortical lesions. Indeed, MRI using various sequences allows an evaluation of several imaging markers related to SVD, including recent small subcortical infarcts, WMH, lacunes, enlarged perivascular spaces (PVS), cerebral microbleeds (CMB), and brain atrophy, as summarized by an international working group defining neuroimaging standards (STRIVE) for research into SVDPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5XYXJkbGF3PC9BdXRob3I+PFllYXI+MjAxMzwvWWVhcj48
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ADDIN EN.CITE.DATA 5.The uncertain or lower reproducibility of these MRI markers, especially across centres, is a major concern in cross-sectional and longitudinal observational studies as well as in clinical trials. Indeed, a key challenge is to minimize the variability caused by technological and subject-related factors. Such variability may confound the detection of disease- or treatment-related MRI changes, thereby limiting the power to detect and follow the progression of imaging markers of SVD. From the patient being imaged to the final imaging marker of interest, which is quantified by an expert rater or an automatic image-processing algorithm, there are several sources of variability. These can include subject-related physiological changes or instrument-related factors such as field strength, head coil, gradients and sequence parameters (see Fig. 1). Each can affect the reproducibility of the marker. Additional variation may be introduced at the image post-processing stage, depending on which software is used for inhomogeneities correction or tissue segmentation, for example. The definition of lesions and the interpretation of images by several experts may be other sources of variability. The measurement of each MRI marker therefore depends on several factors, the effects of which need to be minimized, corrected or taken into account in a posteriori analyses for studies to be comparable.The aims of this review are to summarise current knowledge regarding the reproducibility and variability of quantitative imaging markers of SVD including WMH, lacunes, PVS, CMB and brain volume in both single centre and multicentre contexts. To highlight technical solutions for common problems and to identify the remaining challenges that need to be overcome for such markers to be considered valid and reliable. Other SVD-related MRI markers, due to their acute manifestation (recent small subcortical infarct) or their so far non-quantitative assessment (cortical superficial siderosis, cortical microinfarcts) have not been examined in reproducibility studies to our knowledge and hence were not included in the literature search. Post-processing tools or expert ratings that also contribute to the global variability and accuracy of the final marker measurement are beyond the scope of this review. For each MRI marker of SVD we review: 1) its reproducibility in studies with a scan-rescan design in single or across multiple centres; 2) the acquisition-related sources of variability; and, 3) the techniques that can be used to minimise methodological limitations. We also discuss those factors that need to be considered to deal with image variability inherent in MRI markers in multicentre design studies such as clinical trials or longitudinal studies.MethodsSearch strategy and selection criteriaThis review was designed to focus on the reproducibility and variability of well-defined MRI markers of SVD: WMH, lacunes, PVS, CMB and brain volume.For each biomarker, taking into account the vocabulary heterogeneity of each marker (see supplementary material), a PubMed search has been conducted up to August 2015 14th with the associated terms “MRI” AND (“variability” OR “reproducibility” OR “reliability” OR “consistency”). Papers published before 2002 were not considered to focus on recent MR technology. Reviews, conference abstracts and articles not written in english were excluded from analysis. Articles from our personal databases were also incorporated to the search. To be included in the reproducibility summary (Table 1), studies had to include subjects who underwent a scan-rescan procedure either at the same centre or/and at different centres. Filtering was firstly based on review of the title and abstract for relevance. Studies were not mandatorily related to SVD (e.g., multiple sclerosis or AD) to include the largest number of studies about variability and reliability for the MRI markers of interest.Term and metrics definitionsAs the development and implementation of quantitative imaging biomarkers has been hampered in part by inconsistent use of terminology, an interdisciplinary group of radiologists, statisticians, physicists, and other researchers worked to develop a comprehensive terminology to serve as a foundation for quantitative imaging biomarker claimsPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5LZXNzbGVyPC9BdXRob3I+PFllYXI+MjAxNTwvWWVhcj48
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ADDIN EN.CITE.DATA 6. Definitions agreed are reproduced in supplementary material. In order to simplify the manuscript reading, we will only use the terms “within-centre reproducibility” (strictly speaking, the repeatability) to report results from scan-rescan procedures in a single centre; and “between-centre reproducibility” to report results from multicentre studies. Several metrics are used to measure variability or reproducibility across different studies. A brief description of each is included in supplementary material.Results3.1 Variation in the number of studies and in the evaluation of the reproducibility Number of studiesThere was large variation in the number of studies conducted for assessing the reproducibility and variability of each SVD MRI marker. Brain volume and brain atrophy measures have received the most attention (19 studies with within or/and between centre repeated scans of the same subjects)PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TY2huYWNrPC9BdXRob3I+PFllYXI+MjAwNDwvWWVhcj48
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ADDIN EN.CITE.DATA 7-22. This is probably related to the significant imaging research efforts in AD community during the last decade in which cerebral atrophy was of primary concern. Particularly, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) permitted the most important studies on this aspect, including repeated examinations of the same subject on the same or on different scanners PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5LcnVnZ2VsPC9BdXRob3I+PFllYXI+MjAxMDwvWWVhcj48
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ADDIN EN.CITE.DATA 14, 17. Only five studies were found that evaluated the within-centre or between-centre variability of WMH measuresPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5XZWk8L0F1dGhvcj48WWVhcj4yMDAyPC9ZZWFyPjxSZWNO
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ADDIN EN.CITE.DATA 25, 27-29 and no study on the variability of PVS and lacunes. This probably reflects the absence of consensus until recently about radiological terminology and definitions as well as difficulties to automatically quantify these markers ADDIN EN.CITE <EndNote><Cite><Author>Potter</Author><Year>2011</Year><RecNum>1424</RecNum><DisplayText><style face="superscript">30</style></DisplayText><record><rec-number>1424</rec-number><foreign-keys><key app="EN" db-id="0w0d5w0vt0areaeafstvvw01vaa9s5ww2r2e">1424</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Potter, G. M.</author><author>Marlborough, F. J.</author><author>Wardlaw, J. M.</author></authors></contributors><auth-address>Division of Clinical Neurosciences, Western General Hospital, Edinburgh EH4 2XU, UK.</auth-address><titles><title>Wide variation in definition, detection, and description of lacunar lesions on imaging</title><secondary-title>Stroke</secondary-title></titles><periodical><full-title>Stroke</full-title></periodical><pages>359-66</pages><volume>42</volume><number>2</number><edition>2011/01/05</edition><keywords><keyword>Brain Infarction/*classification/*diagnosis</keyword><keyword>Diagnostic Imaging/*methods/*standards</keyword><keyword>Humans</keyword><keyword>Magnetic Resonance Imaging/methods/standards</keyword><keyword>Random Allocation</keyword></keywords><dates><year>2011</year><pub-dates><date>Feb</date></pub-dates></dates><isbn>1524-4628 (Electronic)
0039-2499 (Linking)</isbn><accession-num>21193752</accession-num><urls><related-urls><url> [pii]
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ADDIN EN.CITE.DATA 5, 31. Many qualitative and quantitative techniques have been used to measure WMH. Qualitative approaches are based on visual rating scales completed by well-trained readersPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5GYXpla2FzPC9BdXRob3I+PFllYXI+MTk4NzwvWWVhcj48
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ADDIN EN.CITE.DATA 41. In 10 patients with SVD, Theysohn et al. showed that FLAIR images acquired at 7T highlighted WMH known from 1.5T with comparable extent ADDIN EN.CITE <EndNote><Cite><Author>Theysohn</Author><Year>2011</Year><RecNum>41</RecNum><DisplayText><style face="superscript">25</style></DisplayText><record><rec-number>41</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">41</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Theysohn, J. M.</author><author>Kraff, O.</author><author>Maderwald, S.</author><author>Barth, M.</author><author>Ladd, S. C.</author><author>Forsting, M.</author><author>Ladd, M. E.</author><author>Gizewski, E. R.</author></authors></contributors><titles><title>7 Tesla MRI of Microbleeds and White Matter Lesions as Seen in Vascular Dementia</title><secondary-title>Journal of Magnetic Resonance Imaging</secondary-title></titles><periodical><full-title>Journal of Magnetic Resonance Imaging</full-title></periodical><pages>782-791</pages><volume>33</volume><number>4</number><dates><year>2011</year><pub-dates><date>Apr</date></pub-dates></dates><isbn>1053-1807</isbn><accession-num>WOS:000288913200003</accession-num><urls><related-urls><url><Go to ISI>://WOS:000288913200003</url></related-urls></urls><electronic-resource-num>10.1002/jmri.22513</electronic-resource-num></record></Cite></EndNote>25. However, WMH were not adequately quantified in this study as neither number nor volume was assessed. On the contrary, in patients with multiple sclerosis, it has been reported that 3T scans showed a mean 10% higher WMH volume compared to 1.5T scans in total lesion volume based on PD-weighted imagesPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TaWNvdHRlPC9BdXRob3I+PFllYXI+MjAwMzwvWWVhcj48
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ADDIN EN.CITE.DATA 24.Sequence effectWMH detection and automated quantification usually relies on the FLAIR sequence, which produces T2-weighted images with cerebrospinal fluid suppression. It has been recently shown that 3D FLAIR sequence parameters could be optimized to increase lesion detection and identification in multiple sclerosis compared with default 3D FLAIR or 2D FLAIR (Fig. 2) ADDIN EN.CITE <EndNote><Cite><Author>Polak</Author><Year>2012</Year><RecNum>37</RecNum><DisplayText><style face="superscript">42</style></DisplayText><record><rec-number>37</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">37</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Polak, P.</author><author>Magnano, C.</author><author>Zivadinov, R.</author><author>Poloni, G.</author></authors></contributors><titles><title>3D FLAIRED: 3D fluid attenuated inversion recovery for enhanced detection of lesions in multiple sclerosis</title><secondary-title>Magnetic Resonance in Medicine</secondary-title></titles><periodical><full-title>Magnetic Resonance in Medicine</full-title></periodical><pages>874-881</pages><volume>68</volume><number>3</number><dates><year>2012</year><pub-dates><date>Sep</date></pub-dates></dates><isbn>0740-3194</isbn><accession-num>WOS:000308098100025</accession-num><urls><related-urls><url><Go to ISI>://WOS:000308098100025</url></related-urls></urls><electronic-resource-num>10.1002/mrm.23289</electronic-resource-num></record></Cite></EndNote>42. This approach might also be used in SVD. Indirectly, this study showed that MR parameters (echo time, inversion time) influences the tissue contrast between the normal white matter and white matter lesions on which the algorithms to segment WMH heavily depend.Some pipelines have used an interleaved PD/T2 acquisition for WMH segmentation, especially as this dual spin echo sequence is more sensitive to WMH in the deep basal ganglia and thalamus than FLAIR and also allows better detection of perivascular spaces60,PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5SYW1pcmV6PC9BdXRob3I+PFllYXI+MjAxNTwvWWVhcj48
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ADDIN EN.CITE.DATA 43. Multisequence image analysis approaches to quantify WMH, eg which combine FLAIR with T2* or T1 or T2 or several sequences together, are increasingly used and emphasise the need to obtain several key standard sequences when studying SVD.Partial volume effectsPartial volume effect is the combination of signals from different tissue types in a single voxel whose image intensity is thus dependent on proportions of each tissue. Partial volume effects with clinically feasible resolutions complicate delineation of the lesion borders ADDIN EN.CITE <EndNote><Cite><Author>Garcia-Lorenzo</Author><Year>2013</Year><RecNum>95</RecNum><DisplayText><style face="superscript">44</style></DisplayText><record><rec-number>95</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">95</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Garcia-Lorenzo, D.</author><author>Francis, S.</author><author>Narayanan, S.</author><author>Arnold, D. L.</author><author>Collins, D. L.</author></authors></contributors><auth-address>McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Canada. dgarcia@bic.mni.mcgill.ca</auth-address><titles><title>Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging</title><secondary-title>Med Image Anal</secondary-title><alt-title>Medical image analysis</alt-title></titles><alt-periodical><full-title>Medical Image Analysis</full-title></alt-periodical><pages>1-18</pages><volume>17</volume><number>1</number><edition>2012/10/23</edition><keywords><keyword>Brain/ pathology</keyword><keyword>Humans</keyword><keyword>Magnetic Resonance Imaging/ methods</keyword><keyword>Multiple Sclerosis/ pathology</keyword></keywords><dates><year>2013</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>1361-8423 (Electronic)
1361-8415 (Linking)</isbn><accession-num>23084503</accession-num><urls></urls><electronic-resource-num>10.1016/j.media.2012.09.004</electronic-resource-num><remote-database-provider>NLM</remote-database-provider><language>eng</language></record></Cite></EndNote>44. Particularly, smaller voxel sizes allow more accurate estimation and yield larger total lesion volumes, especially for patients with small lesions ADDIN EN.CITE <EndNote><Cite><Author>Filippi</Author><Year>1995</Year><RecNum>57</RecNum><DisplayText><style face="superscript">45</style></DisplayText><record><rec-number>57</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">57</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Filippi, M.</author><author>Horsfield, M. A.</author><author>Campi, A.</author><author>Mammi, S.</author><author>Pereira, C.</author><author>Comi, G.</author></authors></contributors><auth-address>UNIV MILAN,OSPED SAN RAFFAELE,INST SCI,DEPT NEURORADIOL,I-20127 MILAN,ITALY. UNIV LEICESTER,LEICESTER ROYAL INFIRM,DEPT MED PHYS,LEICESTER,LEICS,ENGLAND.
FILIPPI, M (reprint author), UNIV MILAN,OSPED SAN RAFFAELE,INST SCI,DEPT NEUROL,VIA OLGETTINA 60,I-20127 MILAN,ITALY.</auth-address><titles><title>RESOLUTION-DEPENDENT ESTIMATES OF LESION VOLUMES IN MAGNETIC-RESONANCE-IMAGING STUDIES OF THE BRAIN IN MULTIPLE-SCLEROSIS</title><secondary-title>Annals of Neurology</secondary-title></titles><periodical><full-title>Annals of Neurology</full-title></periodical><pages>749-754</pages><volume>38</volume><number>5</number><keywords><keyword>interferon therapy</keyword><keyword>mri</keyword></keywords><dates><year>1995</year><pub-dates><date>Nov</date></pub-dates></dates><isbn>0364-5134</isbn><accession-num>WOS:A1995TE95500008</accession-num><work-type>Article</work-type><urls><related-urls><url><Go to ISI>://WOS:A1995TE95500008</url><url>. For example, in 10 patients with multiple sclerosis, lesion volumes were reported to be on average 9% greater with 3 mm compared to 5 mm slice thickness ADDIN EN.CITE <EndNote><Cite><Author>Filippi</Author><Year>1995</Year><RecNum>57</RecNum><DisplayText><style face="superscript">45</style></DisplayText><record><rec-number>57</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">57</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Filippi, M.</author><author>Horsfield, M. A.</author><author>Campi, A.</author><author>Mammi, S.</author><author>Pereira, C.</author><author>Comi, G.</author></authors></contributors><auth-address>UNIV MILAN,OSPED SAN RAFFAELE,INST SCI,DEPT NEURORADIOL,I-20127 MILAN,ITALY. UNIV LEICESTER,LEICESTER ROYAL INFIRM,DEPT MED PHYS,LEICESTER,LEICS,ENGLAND.
FILIPPI, M (reprint author), UNIV MILAN,OSPED SAN RAFFAELE,INST SCI,DEPT NEUROL,VIA OLGETTINA 60,I-20127 MILAN,ITALY.</auth-address><titles><title>RESOLUTION-DEPENDENT ESTIMATES OF LESION VOLUMES IN MAGNETIC-RESONANCE-IMAGING STUDIES OF THE BRAIN IN MULTIPLE-SCLEROSIS</title><secondary-title>Annals of Neurology</secondary-title></titles><periodical><full-title>Annals of Neurology</full-title></periodical><pages>749-754</pages><volume>38</volume><number>5</number><keywords><keyword>interferon therapy</keyword><keyword>mri</keyword></keywords><dates><year>1995</year><pub-dates><date>Nov</date></pub-dates></dates><isbn>0364-5134</isbn><accession-num>WOS:A1995TE95500008</accession-num><work-type>Article</work-type><urls><related-urls><url><Go to ISI>://WOS:A1995TE95500008</url><url>. Other factors with some effectsThe approach to post-processing for bias field (or B1 inhomogeneities) correction can have an important effect on WMH volume quantification according to the computational method used ADDIN EN.CITE <EndNote><Cite><Author>Valdes Hernandez</Author><Year>in press</Year><RecNum>1693</RecNum><DisplayText><style face="superscript">46</style></DisplayText><record><rec-number>1693</rec-number><foreign-keys><key app="EN" db-id="0w0d5w0vt0areaeafstvvw01vaa9s5ww2r2e">1693</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Valdes Hernandez, M. C.</author><author>Ghandour, D.</author><author>Gonzales-Castro, V.</author><author>Munoz-Maniega, S.</author><author>Armitage, P.A.A.</author><author>Wang, X.</author><author>Doubal, F.</author><author>Wardlaw, J M</author></authors></contributors><titles><title>On the computational assessment of white matter hyperintensity progression: difficulties in method selection and bias field correction performance on images with significant white matter pathology.</title><secondary-title>Neuroradiology</secondary-title></titles><periodical><full-title>Neuroradiology</full-title></periodical><dates><year>in press</year></dates><urls></urls></record></Cite></EndNote>46.Reproducibility in longitudinal studiesNo study was found to assess the reproducibility of any longitudinal measurement of WMH.3.2.2 Lacunes and perivascular spacesDefinition and interest in SVDFrom a radiological perspective, lacunes of presumed vascular origin are round or ovoid, subcortical, fluid-filled cavities of between 3 mm and 15 mm in diameter, consistent with a previous acute small subcortical infarct or haemorrhage in the territory of one perforating arteriolePEVuZE5vdGU+PENpdGU+PEF1dGhvcj5XYXJkbGF3PC9BdXRob3I+PFllYXI+MjAxMzwvWWVhcj48
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ADDIN EN.CITE.DATA 37, 54-56. The first image processing methods for both lacunes and PVS detection were not fully robust and efficient, requiring time-consuming user manual intervention but new methods are more automated and much faster.Within- or between-centre reproducibilityThe literature is very sparse concerning the reproducibility of measurements describing lacunes or PVSPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5IZXJuYW5kZXogTWRlbDwvQXV0aG9yPjxZZWFyPjIwMTM8
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ADDIN EN.CITE.DATA 51. In 20 subjects scanned twice on a 1.5T scanner, one study assessed a good reproducibility for lacunar volume with the used dedicated software but the extent or numbers of lacunes per subject were not specified PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5SYW1pcmV6PC9BdXRob3I+PFllYXI+MjAxMzwvWWVhcj48
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ADDIN EN.CITE.DATA 65-69. We consider in this review all methods to study CMB as a few computational methods have been published and tested in reproducibility studies. Many MRI acquisition factors are expected to influence CMB detection as their size is a consequence of susceptibility effects that are highly dependent on MRI sequence parameters. In fact, hypointensities are typically larger than the physical size of the corresponding histopathological lesion (blooming effect)PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TY2hyYWc8L0F1dGhvcj48WWVhcj4yMDEwPC9ZZWFyPjxS
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ADDIN EN.CITE.DATA 70. Within- or between-centre reproducibilityNo study was found to assess CMB detection reproducibility through a scan-rescan procedure at the same centre or between centres with the same imaging protocol. However, several studies examined various factors involved in CMB detection such as magnetic field strength or sequence parameters.Magnetic field effectIntravoxel dephasing due to susceptibility effects and associated signal loss in gradient-echo images increase with higher magnetic fields ADDIN EN.CITE <EndNote><Cite><Author>De Guio</Author><Year>2008</Year><RecNum>1585</RecNum><DisplayText><style face="superscript">71</style></DisplayText><record><rec-number>1585</rec-number><foreign-keys><key app="EN" db-id="0w0d5w0vt0areaeafstvvw01vaa9s5ww2r2e">1585</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>De Guio, F.</author><author>Benoit-Cattin, H.</author><author>Davenel, A.</author></authors></contributors><auth-address>Cemagref, Food Process Engineering Research Unit, 35 044, Rennes, France. francois.de-guio@cemagref.fr</auth-address><titles><title>Signal decay due to susceptibility-induced intravoxel dephasing on multiple air-filled cylinders: MRI simulations and experiments</title><secondary-title>MAGMA</secondary-title><alt-title>Magma (New York, N.Y.)</alt-title></titles><periodical><full-title>MAGMA</full-title><abbr-1>Magma (New York, N.Y.)</abbr-1></periodical><alt-periodical><full-title>MAGMA</full-title><abbr-1>Magma (New York, N.Y.)</abbr-1></alt-periodical><pages>261-71</pages><volume>21</volume><number>4</number><edition>2008/06/26</edition><keywords><keyword>Anisotropy</keyword><keyword>Artifacts</keyword><keyword>Computer Simulation</keyword><keyword>Image Interpretation, Computer-Assisted</keyword><keyword>Magnetic Resonance Imaging/ methods</keyword><keyword>Phantoms, Imaging</keyword></keywords><dates><year>2008</year><pub-dates><date>Jul</date></pub-dates></dates><isbn>0968-5243 (Print)
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ADDIN EN.CITE.DATA 25, 28 as illustrated in Fig. 3. For example, mean increases from 1.5T to 3T were 48% for contrast to noise ratio and 12% for CMB size on 119 CMB detected in 4 subjects70. In addition, more CMB are found at higher field strength; Theysohn et al. found 101 CMB at 7T (mean size: 3.2 mm) against 33 at 1.5T (mean size: 1.4 mm) in 10 subjects ADDIN EN.CITE <EndNote><Cite><Author>Theysohn</Author><Year>2011</Year><RecNum>41</RecNum><DisplayText><style face="superscript">25</style></DisplayText><record><rec-number>41</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">41</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Theysohn, J. M.</author><author>Kraff, O.</author><author>Maderwald, S.</author><author>Barth, M.</author><author>Ladd, S. C.</author><author>Forsting, M.</author><author>Ladd, M. E.</author><author>Gizewski, E. R.</author></authors></contributors><titles><title>7 Tesla MRI of Microbleeds and White Matter Lesions as Seen in Vascular Dementia</title><secondary-title>Journal of Magnetic Resonance Imaging</secondary-title></titles><periodical><full-title>Journal of Magnetic Resonance Imaging</full-title></periodical><pages>782-791</pages><volume>33</volume><number>4</number><dates><year>2011</year><pub-dates><date>Apr</date></pub-dates></dates><isbn>1053-1807</isbn><accession-num>WOS:000288913200003</accession-num><urls><related-urls><url><Go to ISI>://WOS:000288913200003</url></related-urls></urls><electronic-resource-num>10.1002/jmri.22513</electronic-resource-num></record></Cite></EndNote>25.Sequence effect3D T2*-weighted gradient-echo images reveal significantly more CMB in more subjects compared to 2D T2*-weighted gradient-echo images, primarily because of higher spatial resolution ADDIN EN.CITE <EndNote><Cite><Author>Vernooij</Author><Year>2008</Year><RecNum>67</RecNum><DisplayText><style face="superscript">72</style></DisplayText><record><rec-number>67</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">67</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Vernooij, M. W.</author><author>Ikram, M. A.</author><author>Wielopolski, P. A.</author><author>Krestin, G. P.</author><author>Breteler, M. M. B.</author><author>van der Lugt, A.</author></authors></contributors><titles><title>Cerebral microbleeds: Accelerated 3D T2*-weighted GRE MR imaging versus conventional 2D T2*-weighted GRE MR imaging for detection</title><secondary-title>Radiology</secondary-title></titles><periodical><full-title>Radiology</full-title></periodical><pages>272-277</pages><volume>248</volume><number>1</number><dates><year>2008</year><pub-dates><date>Jul</date></pub-dates></dates><isbn>0033-8419</isbn><accession-num>WOS:000256978900031</accession-num><urls><related-urls><url><Go to ISI>://WOS:000256978900031</url></related-urls></urls><electronic-resource-num>10.1148/radiol.2481071158</electronic-resource-num></record></Cite></EndNote>72. In 200 older aged participants, Vernooij et al. reported 71 subjects with at least one microbleed (median number 2.5) using the 3D sequence compared to 42 subjects (median number 1.0) with the 2D sequence. Likewise, the susceptibility weighted imaging (SWI) technique, which uses phase images to enhance susceptibility-related contrasts in the resulting image, is particularly sensitive to paramagnetic and ferromagnetic substancesPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5IYWFja2U8L0F1dGhvcj48WWVhcj4yMDA0PC9ZZWFyPjxS
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ADDIN EN.CITE.DATA 73. CMB are better delineated on SWI images compared to classic T2* imagesPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5OYW5kaWdhbTwvQXV0aG9yPjxZZWFyPjIwMDk8L1llYXI+
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ADDIN EN.CITE.DATA 76. To note, the differentiation between CMB and vessels might be more difficult with SWI compared to T2* images although this is a matter of debate PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5DaGFyaWRpbW91PC9BdXRob3I+PFllYXI+MjAxMjwvWWVh
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ADDIN EN.CITE.DATA 75, 77. A last point is that susceptibility-weighted sequences are different according to vendors (e.g., ‘SWI’ for Siemens, ‘SWAN’ for General Electric, ‘VenoBOLD’ for Philips) and only the Siemens ‘SWI’ technique uses phase data. Therefore, the effect of vendor’s susceptibility-weighted technique on CMB detection needs further assessment.Sequence parameters effectThe echo time (TE) reflects the timescale on which dephasing occurs, with long echo times enabling more time for dephasing thereby enlarging the susceptibility effect. Thus, choice of TE directly impacts on the number and volume of microbleeds detected as well as the number of structures that may be mistaken for microbleeds (calcium or iron deposits, flow voids, etc.). Increasing TE improves the sensitivity to detect CMB but more artefacts are generated near air-tissues interfaces causing uncertainty about rating some lesionsPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5HcmVnb2lyZTwvQXV0aG9yPjxZZWFyPjIwMTA8L1llYXI+
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ADDIN EN.CITE.DATA 81, 86, a monogenic SVD ADDIN EN.CITE <EndNote><Cite><Author>Chabriat</Author><Year>2009</Year><RecNum>1016</RecNum><DisplayText><style face="superscript">2</style></DisplayText><record><rec-number>1016</rec-number><foreign-keys><key app="EN" db-id="0w0d5w0vt0areaeafstvvw01vaa9s5ww2r2e">1016</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Chabriat, Hugues</author><author>Joutel, Anne</author><author>Dichgans, Martin</author><author>Tournier-Lasserve, Elizabeth</author><author>Bousser, Marie-Germaine</author></authors></contributors><auth-address>Service de Neurologie, Groupe Hospitalier Lariboisière-Fernand-Widal, Assistance Publique H?pitaux de Paris, F-75010, Paris, France.</auth-address><titles><title>Cadasil</title><secondary-title>Lancet Neurology</secondary-title></titles><periodical><full-title>Lancet neurology</full-title></periodical><pages>643-653</pages><volume>8</volume><number>7</number><dates><year>2009</year><pub-dates><date>Jul 01</date></pub-dates></dates><urls><related-urls><url>(09)70127-9</electronic-resource-num><language>eng</language></record></Cite></EndNote>2. In sporadic forms, brain volume was notably associated with the presence and progression of lacunesPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5LbG9wcGVuYm9yZzwvQXV0aG9yPjxZZWFyPjIwMTI8L1ll
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ADDIN EN.CITE.DATA 88, 89, SPM ADDIN EN.CITE <EndNote><Cite><Author>Ashburner</Author><Year>2005</Year><RecNum>48</RecNum><DisplayText><style face="superscript">90</style></DisplayText><record><rec-number>48</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">48</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Ashburner, J.</author><author>Friston, K. J.</author></authors></contributors><auth-address>Wellcome Dept Imaging Neurosci, Funct Imaging Lab, London WC1N 3BG, England.
Ashburner, J (reprint author), Wellcome Dept Imaging Neurosci, Funct Imaging Lab, 12 Queen Sq, London WC1N 3BG, England.
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ADDIN EN.CITE.DATA 91, BrainVisa ADDIN EN.CITE <EndNote><Cite><Author>Mangin</Author><Year>2004</Year><RecNum>494</RecNum><DisplayText><style face="superscript">92</style></DisplayText><record><rec-number>494</rec-number><foreign-keys><key app="EN" db-id="0w0d5w0vt0areaeafstvvw01vaa9s5ww2r2e">494</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Mangin, J-F</author><author>Rivière, D</author><author>Cachia, A</author><author>Duchesnay, E</author><author>Cointepas, Y</author><author>Papadopoulos-Orfanos, D</author><author>Scifo, P</author><author>Ochiai, T</author><author>Brunelle, F</author><author>Régis, J</author></authors></contributors><auth-address>Service Hospitalier Frédéric Joliot, CEA, 91401 Orsay cedex, France. mangin@shfj.cea.fr</auth-address><titles><title>A framework to study the cortical folding patterns</title><secondary-title>NeuroImage</secondary-title></titles><periodical><full-title>NeuroImage</full-title></periodical><pages>S129-38</pages><volume>23 Suppl 1</volume><dates><year>2004</year><pub-dates><date>Feb 01</date></pub-dates></dates><urls><pdf-urls><url>). Table 1 presents per study the numbers of centres, subjects, rescans per subject and main results for reproducibility. For example, in a large cohort of 671 subjects who underwent two successive 3D T1 weighted scans, the median of the relative absolute difference in brain volume was found to be less than 1% ADDIN EN.CITE <EndNote><Cite><Author>Cover</Author><Year>2011</Year><RecNum>16</RecNum><DisplayText><style face="superscript">17</style></DisplayText><record><rec-number>16</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">16</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Cover, K. S.</author><author>van Schijndel, R. A.</author><author>van Dijk, B. W.</author><author>Redolfi, A.</author><author>Knol, D. L.</author><author>Frisoni, G. B.</author><author>Barkhof, F.</author><author>Vrenken, H.</author><author>NeuGrid,</author><author>Alzheimer's Dis, Neuroimaging</author></authors></contributors><titles><title>Assessing the reproducibility of the SienaX and Siena brain atrophy measures using the ADNI back-to-back MP-RAGE MRI scans</title><secondary-title>Psychiatry Research-Neuroimaging</secondary-title></titles><periodical><full-title>Psychiatry Research-Neuroimaging</full-title></periodical><pages>182-190</pages><volume>193</volume><number>3</number><dates><year>2011</year><pub-dates><date>Sep</date></pub-dates></dates><isbn>0925-4927</isbn><accession-num>WOS:000295073400008</accession-num><urls><related-urls><url><Go to ISI>://WOS:000295073400008</url></related-urls></urls><electronic-resource-num>10.1016/j.pscychresns.2011.02.012</electronic-resource-num></record></Cite></EndNote>17. However, the 90th percentile of the absolute value of the difference was reported at 5.11%, which is more than twice the value that would be expected for a Gaussian distribution. This highlights the importance of studying several statistical outcomes to infer the reproducibility beyond the median or mean value of the difference. Thus, little variability is added when measuring brain volume in a single centre without changing the conditions of acquisition, at least regarding commonly reported disease-related changes. It is worth noting that most studies with a scan-rescan design included small samples (all n< 50 except Cover et al. ADDIN EN.CITE <EndNote><Cite><Author>Cover</Author><Year>2011</Year><RecNum>16</RecNum><DisplayText><style face="superscript">17</style></DisplayText><record><rec-number>16</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">16</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Cover, K. S.</author><author>van Schijndel, R. A.</author><author>van Dijk, B. W.</author><author>Redolfi, A.</author><author>Knol, D. L.</author><author>Frisoni, G. 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ADDIN EN.CITE.DATA 16). Between-centre reproducibility (effect of various vendors, magnetic field strengths, coils) Effects of combining several MRI vendors, static magnetic field strengths or coil designs on brain volumetry can be assessed in studies where subjects are scanned several times in different centres (Table 1). As expected, compared to the within-centre variability, the between-centre volume variability was much higher at 1.5TPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TY2huYWNrPC9BdXRob3I+PFllYXI+MjAwNDwvWWVhcj48
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ADDIN EN.CITE.DATA 10, 12, 14, 15, 18. In one of the largest multicentre studies with a scan-rescan procedure (a subset of ADNI database in which 172 subjects were rescanned once in 59 different MRI sites on 17 different scanner types), the effects of scanner hardware and imaging protocol on volume quantification were analyzed ADDIN EN.CITE <EndNote><Cite><Author>Kruggel</Author><Year>2010</Year><RecNum>11</RecNum><DisplayText><style face="superscript">14</style></DisplayText><record><rec-number>11</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">11</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Kruggel, F.</author><author>Turner, J.</author><author>Muftuler, L. T.</author><author>Alzheimers Dis Neuroimaging, Initia</author></authors></contributors><titles><title>Impact of scanner hardware and imaging protocol on image quality and compartment volume precision in the ADNI cohort</title><secondary-title>Neuroimage</secondary-title></titles><periodical><full-title>Neuroimage</full-title></periodical><pages>2123-2133</pages><volume>49</volume><number>3</number><dates><year>2010</year><pub-dates><date>Feb</date></pub-dates></dates><isbn>1053-8119</isbn><accession-num>WOS:000273626400019</accession-num><urls><related-urls><url><Go to ISI>://WOS:000273626400019</url></related-urls></urls><electronic-resource-num>10.1016/j.neuroimage.2009.11.006</electronic-resource-num></record></Cite></EndNote>14. The most striking result was that the variance in volumetric measures was 10 times higher when subjects were imaged on different scanners compared to acquisitions on the same scanners. For example, for brain volume, the median (standard deviation) of the absolute within-subject variability was 0.3% (0.5%) vs. 4.8% (4.6%) for repeated scans on the same scanner vs. on different scanners. This was essentially explained by the scanner-dependent geometrical inaccuracies and by the differences in the gray to white matter tissue contrast from variation in the imaging sequences used and RF coils (while this contrast is obviously impacting gray matter and white matter volumes, it also influences the total brain volume computation ADDIN EN.CITE <EndNote><Cite><Author>Kruggel</Author><Year>2010</Year><RecNum>11</RecNum><DisplayText><style face="superscript">14</style></DisplayText><record><rec-number>11</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">11</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Kruggel, F.</author><author>Turner, J.</author><author>Muftuler, L. T.</author><author>Alzheimers Dis Neuroimaging, Initia</author></authors></contributors><titles><title>Impact of scanner hardware and imaging protocol on image quality and compartment volume precision in the ADNI cohort</title><secondary-title>Neuroimage</secondary-title></titles><periodical><full-title>Neuroimage</full-title></periodical><pages>2123-2133</pages><volume>49</volume><number>3</number><dates><year>2010</year><pub-dates><date>Feb</date></pub-dates></dates><isbn>1053-8119</isbn><accession-num>WOS:000273626400019</accession-num><urls><related-urls><url><Go to ISI>://WOS:000273626400019</url></related-urls></urls><electronic-resource-num>10.1016/j.neuroimage.2009.11.006</electronic-resource-num></record></Cite></EndNote>14). Such geometric distortions originate from gradient field nonlinearity (in theory, a gradient system is supposed to produce an incremental magnetic field varying linearly with distance from isocenter) and the static field inhomogeneity, whilst the contrast between brain tissues is determined by a variety of sequence parameters. Both factors are dependent on the scanner hardware (a combination of vendor, magnetic field and coil systems), which explained 30 to 50% of the variance of any volumetric measure in that study. In another study, 15 subjects underwent 4 scan sessions at 3 sites with two different vendors ADDIN EN.CITE <EndNote><Cite><Author>Jovicich</Author><Year>2009</Year><RecNum>12</RecNum><DisplayText><style face="superscript">10</style></DisplayText><record><rec-number>12</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">12</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Jovicich, J.</author><author>Czanner, S.</author><author>Han, X.</author><author>Salat, D.</author><author>van der Kouwe, A.</author><author>Quinn, B.</author><author>Pacheco, J.</author><author>Albert, M.</author><author>Killiany, R.</author><author>Blacker, D.</author><author>Maguire, P.</author><author>Rosas, D.</author><author>Makris, N.</author><author>Gollub, R.</author><author>Dale, A.</author><author>Dickerson, B. C.</author><author>Fischl, B.</author></authors></contributors><titles><title>MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: Reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths</title><secondary-title>Neuroimage</secondary-title></titles><periodical><full-title>Neuroimage</full-title></periodical><pages>177-192</pages><volume>46</volume><number>1</number><dates><year>2009</year><pub-dates><date>May</date></pub-dates></dates><isbn>1053-8119</isbn><accession-num>WOS:000265723800020</accession-num><urls><related-urls><url><Go to ISI>://WOS:000265723800020</url><url>. Interestingly, combining data of different vendors and/or field strength did not significantly change the standard deviation of the volume differences across conditions relative to the test–retest reproducibility within a fixed MRI system. However, the mean volume difference did change within one standard deviation from zero for intracranial volume.Upgrades and repairOnly a few studies have examined the effects of MR systems upgrades and repairs on volumetric measurements. It appears that mixing volumes derived from data acquired across a major scanner upgrade (such as replacement of the magnet, gradient system and software) do not show significant changes, although volumes of some specific structures may show a slight scanner specific volume bias ADDIN EN.CITE <EndNote><Cite><Author>Jovicich</Author><Year>2009</Year><RecNum>12</RecNum><DisplayText><style face="superscript">10</style></DisplayText><record><rec-number>12</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">12</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Jovicich, J.</author><author>Czanner, S.</author><author>Han, X.</author><author>Salat, D.</author><author>van der Kouwe, A.</author><author>Quinn, B.</author><author>Pacheco, J.</author><author>Albert, M.</author><author>Killiany, R.</author><author>Blacker, D.</author><author>Maguire, P.</author><author>Rosas, D.</author><author>Makris, N.</author><author>Gollub, R.</author><author>Dale, A.</author><author>Dickerson, B. C.</author><author>Fischl, B.</author></authors></contributors><titles><title>MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: Reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths</title><secondary-title>Neuroimage</secondary-title></titles><periodical><full-title>Neuroimage</full-title></periodical><pages>177-192</pages><volume>46</volume><number>1</number><dates><year>2009</year><pub-dates><date>May</date></pub-dates></dates><isbn>1053-8119</isbn><accession-num>WOS:000265723800020</accession-num><urls><related-urls><url><Go to ISI>://WOS:000265723800020</url><url>. Similarly, consistent measures for brain structure volumes have been found before and after gradient coil replacement in a 3T systemPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5IdWFuZzwvQXV0aG9yPjxZZWFyPjIwMTI8L1llYXI+PFJl
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ADDIN EN.CITE.DATA 94 which can cause major variance between pre- and post-upgrade scans if not detected quickly. Scanner upgrade or replacement are almost impossible to avoid, and therefore centres should consider having longitudinal phantom and volunteer assessments for quality assurance and to calculate differences in brain volumetry across time that would allow conversion factors to be generated.Sequence effectUse of 3D inversion prepared T1-weighted fast gradient-echo sequences for brain tissue segmentation and volumetry is universal nowadays. It is widely used for its contrast properties and is a standard for most MRI vendors. It is therefore easy to implement in multicentre studies. Other sequences such as multi-echo fast low angle shot sequences have similar contrast properties and showed comparable volume reproducibility from Bland-Altman plot analysis than the MPRAGE sequence for example ADDIN EN.CITE <EndNote><Cite><Author>Jovicich</Author><Year>2009</Year><RecNum>12</RecNum><DisplayText><style face="superscript">10</style></DisplayText><record><rec-number>12</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">12</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Jovicich, J.</author><author>Czanner, S.</author><author>Han, X.</author><author>Salat, D.</author><author>van der Kouwe, A.</author><author>Quinn, B.</author><author>Pacheco, J.</author><author>Albert, M.</author><author>Killiany, R.</author><author>Blacker, D.</author><author>Maguire, P.</author><author>Rosas, D.</author><author>Makris, N.</author><author>Gollub, R.</author><author>Dale, A.</author><author>Dickerson, B. C.</author><author>Fischl, B.</author></authors></contributors><titles><title>MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: Reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths</title><secondary-title>Neuroimage</secondary-title></titles><periodical><full-title>Neuroimage</full-title></periodical><pages>177-192</pages><volume>46</volume><number>1</number><dates><year>2009</year><pub-dates><date>May</date></pub-dates></dates><isbn>1053-8119</isbn><accession-num>WOS:000265723800020</accession-num><urls><related-urls><url><Go to ISI>://WOS:000265723800020</url><url>. However, comparison between volumes derived from both sequence types demonstrated significant biases in the mean volume difference, probably due to a differing T2* sensitivity ADDIN EN.CITE <EndNote><Cite><Author>Jovicich</Author><Year>2009</Year><RecNum>12</RecNum><DisplayText><style face="superscript">10</style></DisplayText><record><rec-number>12</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">12</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Jovicich, J.</author><author>Czanner, S.</author><author>Han, X.</author><author>Salat, D.</author><author>van der Kouwe, A.</author><author>Quinn, B.</author><author>Pacheco, J.</author><author>Albert, M.</author><author>Killiany, R.</author><author>Blacker, D.</author><author>Maguire, P.</author><author>Rosas, D.</author><author>Makris, N.</author><author>Gollub, R.</author><author>Dale, A.</author><author>Dickerson, B. C.</author><author>Fischl, B.</author></authors></contributors><titles><title>MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: Reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths</title><secondary-title>Neuroimage</secondary-title></titles><periodical><full-title>Neuroimage</full-title></periodical><pages>177-192</pages><volume>46</volume><number>1</number><dates><year>2009</year><pub-dates><date>May</date></pub-dates></dates><isbn>1053-8119</isbn><accession-num>WOS:000265723800020</accession-num><urls><related-urls><url><Go to ISI>://WOS:000265723800020</url><url> voxels are superior to non-isotropic voxels for accurate estimation of brain volume. In sequences with non-isotropic resolution, the choice of image orientation (sagittal, coronal or axial) naturally yields volumetric differences when comparing datasets acquired with different image orientations ADDIN EN.CITE <EndNote><Cite><Author>Patwardhan</Author><Year>2001</Year><RecNum>96</RecNum><DisplayText><style face="superscript">95</style></DisplayText><record><rec-number>96</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">96</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Patwardhan, A. J.</author><author>Eliez, S.</author><author>Warsofsky, I. S.</author><author>Glover, G. H.</author><author>White, C. D.</author><author>Giedd, J. N.</author><author>Peterson, B. S.</author><author>Rojas, D. C.</author><author>Reiss, A. L.</author></authors></contributors><auth-address>Stanford Psychiatry Neuroimaging Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, CA 94305-5719, USA.</auth-address><titles><title>Effects of image orientation on the comparability of pediatric brain volumes using three-dimensional MR data</title><secondary-title>J Comput Assist Tomogr</secondary-title><alt-title>Journal of computer assisted tomography</alt-title></titles><alt-periodical><full-title>Journal of computer assisted tomography</full-title></alt-periodical><pages>452-7</pages><volume>25</volume><number>3</number><edition>2001/05/15</edition><keywords><keyword>Adolescent</keyword><keyword>Analysis of Variance</keyword><keyword>Brain/ anatomy & histology</keyword><keyword>Child</keyword><keyword>Female</keyword><keyword>Humans</keyword><keyword>Image Processing, Computer-Assisted</keyword><keyword>Least-Squares Analysis</keyword><keyword>Magnetic Resonance Imaging/ methods</keyword><keyword>Male</keyword><keyword>Observer Variation</keyword></keywords><dates><year>2001</year><pub-dates><date>May-Jun</date></pub-dates></dates><isbn>0363-8715 (Print)
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ADDIN EN.CITE.DATA 16. The authors found that the variance assigned to physiological fluctuations was nearly two times greater than the variance due to patient repositioning during scan-rescan. This implies that scan–rescan experiments might only provide a lower bound on the true error in repeated volumetric measurements from MRI exams. It is well known that the hydration status of an individual can affect the measurement of brain volume. Specifically, lack of fluid intake for 16 hours has been found to result in a 0.55% decrease in cerebral volume and re-hydration an increase of 0.72% in brain volume ADDIN EN.CITE <EndNote><Cite><Author>Duning</Author><Year>2005</Year><RecNum>73</RecNum><DisplayText><style face="superscript">96</style></DisplayText><record><rec-number>73</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">73</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Duning, T.</author><author>Kloska, S.</author><author>Steinstrater, O.</author><author>Kugel, H.</author><author>Heindel, W.</author><author>Knecht, S.</author></authors></contributors><auth-address>Department of Neurology, University of Munster, Albert-Schweitzer-Str. 22, 48129 Munster, Germany. duningt@uni-muenster.de</auth-address><titles><title>Dehydration confounds the assessment of brain atrophy</title><secondary-title>Neurology</secondary-title><alt-title>Neurology</alt-title></titles><periodical><full-title>Neurology</full-title></periodical><alt-periodical><full-title>Neurology</full-title></alt-periodical><pages>548-50</pages><volume>64</volume><number>3</number><edition>2005/02/09</edition><keywords><keyword>Adult</keyword><keyword>Atrophy</keyword><keyword>Body Water</keyword><keyword>Brain/ pathology</keyword><keyword>Confounding Factors (Epidemiology)</keyword><keyword>Dehydration/ pathology/therapy</keyword><keyword>False Positive Reactions</keyword><keyword>Female</keyword><keyword>Fluid Therapy</keyword><keyword>Humans</keyword><keyword>Imaging, Three-Dimensional</keyword><keyword>Magnetic Resonance Imaging</keyword><keyword>Male</keyword><keyword>Organ Size</keyword></keywords><dates><year>2005</year><pub-dates><date>Feb 8</date></pub-dates></dates><isbn>1526-632X (Electronic)
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ADDIN EN.CITE.DATA 98. These results suggest potential acquisition time and hydration biases should be randomized or statistically controlled to account for the day-to-day brain volume fluctuations.Factors with no or little effectCorrection for so-called B1 inhomogeneities (variations of image intensity due to the radiofrequency field) was not found to substantially change the reproducibility of whole-brain volumetric measurements ADDIN EN.CITE <EndNote><Cite><Author>Jovicich</Author><Year>2009</Year><RecNum>12</RecNum><DisplayText><style face="superscript">10</style></DisplayText><record><rec-number>12</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">12</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Jovicich, J.</author><author>Czanner, S.</author><author>Han, X.</author><author>Salat, D.</author><author>van der Kouwe, A.</author><author>Quinn, B.</author><author>Pacheco, J.</author><author>Albert, M.</author><author>Killiany, R.</author><author>Blacker, D.</author><author>Maguire, P.</author><author>Rosas, D.</author><author>Makris, N.</author><author>Gollub, R.</author><author>Dale, A.</author><author>Dickerson, B. C.</author><author>Fischl, B.</author></authors></contributors><titles><title>MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: Reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths</title><secondary-title>Neuroimage</secondary-title></titles><periodical><full-title>Neuroimage</full-title></periodical><pages>177-192</pages><volume>46</volume><number>1</number><dates><year>2009</year><pub-dates><date>May</date></pub-dates></dates><isbn>1053-8119</isbn><accession-num>WOS:000265723800020</accession-num><urls><related-urls><url><Go to ISI>://WOS:000265723800020</url><url>, unlike its effect on WMH and focal infarcts ADDIN EN.CITE <EndNote><Cite><Author>Wardlaw</Author><Year>2015</Year><RecNum>1690</RecNum><DisplayText><style face="superscript">99</style></DisplayText><record><rec-number>1690</rec-number><foreign-keys><key app="EN" db-id="0w0d5w0vt0areaeafstvvw01vaa9s5ww2r2e">1690</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Wardlaw, J. M.</author><author>Valdes Hernandez, M. C.</author><author>Munoz-Maniega, S.</author></authors></contributors><auth-address>Division of Neuroimaging Sciences and Brain Research Imaging Centre, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom (J.M.W., M.C.V.H., S.M.M.).</auth-address><titles><title>What are white matter hyperintensities made of? Relevance to vascular cognitive impairment</title><secondary-title>J Am Heart Assoc</secondary-title><alt-title>Journal of the American Heart Association</alt-title></titles><periodical><full-title>J Am Heart Assoc</full-title><abbr-1>Journal of the American Heart Association</abbr-1></periodical><alt-periodical><full-title>J Am Heart Assoc</full-title><abbr-1>Journal of the American Heart Association</abbr-1></alt-periodical><pages>001140</pages><volume>4</volume><number>6</number><edition>2015/06/25</edition><dates><year>2015</year><pub-dates><date>Jun</date></pub-dates></dates><isbn>2047-9980 (Electronic)
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ADDIN EN.CITE.DATA 12.3.2.5 Brain atrophyDefinition and interest in SVDBrain atrophy is the difference of total brain volume between two time points computed by a dedicated image processing software from high-resolution anatomical images. Quantitative measures derived from high-resolution anatomical images are considered over atrophy scales (not covered in the present review) as the quantitative measures have better sensitivity to change over time ADDIN EN.CITE <EndNote><Cite><Author>Ross</Author><Year>2015</Year><RecNum>579</RecNum><DisplayText><style face="superscript">101</style></DisplayText><record><rec-number>579</rec-number><foreign-keys><key app="EN" db-id="aapftssat9zp0aepatvp9x090ar5d9925wwd" timestamp="1448534320">579</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Ross, D. E.</author><author>Ochs, A. L.</author><author>DeSmit, M. E.</author><author>Seabaugh, J. M.</author><author>Havranek, M. D.</author></authors></contributors><auth-address>From the Virginia Institute of Neuropsychiatry, Midlothian, VA (DER, ALO, MED, JMS); Virginia Commonwealth University, Richmond, VA (DER, ALO); and Amicus Visual Solutions, Richmond, VA (MDH).</auth-address><titles><title>Man Versus Machine Part 2: Comparison of Radiologists' Interpretations and NeuroQuant Measures of Brain Asymmetry and Progressive Atrophy in Patients With Traumatic Brain Injury</title><secondary-title>J Neuropsychiatry Clin Neurosci</secondary-title><alt-title>The Journal of neuropsychiatry and clinical neurosciences</alt-title></titles><periodical><full-title>J Neuropsychiatry Clin Neurosci</full-title><abbr-1>The Journal of neuropsychiatry and clinical neurosciences</abbr-1></periodical><alt-periodical><full-title>J Neuropsychiatry Clin Neurosci</full-title><abbr-1>The Journal of neuropsychiatry and clinical neurosciences</abbr-1></alt-periodical><pages>147-52</pages><volume>27</volume><number>2</number><edition>2015/04/30</edition><dates><year>2015</year><pub-dates><date>Spring</date></pub-dates></dates><isbn>0895-0172</isbn><accession-num>25923853</accession-num><urls></urls><electronic-resource-num>10.1176/appi.neuropsych.13040088</electronic-resource-num><remote-database-provider>NLM</remote-database-provider><language>eng</language></record></Cite></EndNote>101. Surprisingly, very few studies reported brain atrophy measurement in SVD cohortsPEVuZE5vdGU+PENpdGUgRXhjbHVkZVllYXI9IjEiPjxBdXRob3I+Uml0Y2hpZTwvQXV0aG9yPjxS
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ADDIN EN.CITE.DATA 104, 105 and the Boundary Shift Integral (BSI), that is an automated method that determines the total volume through which the boundaries of a given cerebral structure have moved ADDIN EN.CITE <EndNote><Cite><Author>Freeborough</Author><Year>1997</Year><RecNum>47</RecNum><DisplayText><style face="superscript">106</style></DisplayText><record><rec-number>47</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">47</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Freeborough, P. A.</author><author>Fox, N. C.</author></authors></contributors><auth-address>Freeborough, PA (reprint author), UCL NATL HOSP NEUROL & NEUROSURG, DEMENTIA RES GRP, 8-11 QUEEN SQ, LONDON WC1N 3BG, ENGLAND.</auth-address><titles><title>The boundary shift integral: An accurate and robust measure of cerebral volume changes from registered repeat MRI</title><secondary-title>Ieee Transactions on Medical Imaging</secondary-title></titles><periodical><full-title>Ieee Transactions on Medical Imaging</full-title></periodical><pages>623-629</pages><volume>16</volume><number>5</number><keywords><keyword>atrophy</keyword><keyword>brain</keyword><keyword>MRI</keyword><keyword>registration</keyword><keyword>registration</keyword><keyword>images</keyword><keyword>brain</keyword></keywords><dates><year>1997</year><pub-dates><date>Oct</date></pub-dates></dates><isbn>0278-0062</isbn><accession-num>WOS:A1997YE91900016</accession-num><work-type>Article</work-type><urls><related-urls><url><Go to ISI>://WOS:A1997YE91900016</url></related-urls></urls><electronic-resource-num>10.1109/42.640753</electronic-resource-num><language>English</language></record></Cite></EndNote>106. One alternative method to assess brain atrophy is by performing tissue segmentation at both time points, but this approach suffers from a higher variability given the added variability of each measurementPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5EdXJhbmQtRHViaWVmPC9BdXRob3I+PFllYXI+MjAxMjwv
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ADDIN EN.CITE.DATA 22.Within-centre reproducibilityWithin-centre reproducibility of quantitative image-processing methods for atrophy assessment have been shown to be relatively high in several studies (Table 1) ADDIN EN.CITE <EndNote><Cite><Author>Cover</Author><Year>2011</Year><RecNum>16</RecNum><DisplayText><style face="superscript">17</style></DisplayText><record><rec-number>16</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">16</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Cover, K. S.</author><author>van Schijndel, R. A.</author><author>van Dijk, B. W.</author><author>Redolfi, A.</author><author>Knol, D. L.</author><author>Frisoni, G. B.</author><author>Barkhof, F.</author><author>Vrenken, H.</author><author>NeuGrid,</author><author>Alzheimer's Dis, Neuroimaging</author></authors></contributors><titles><title>Assessing the reproducibility of the SienaX and Siena brain atrophy measures using the ADNI back-to-back MP-RAGE MRI scans</title><secondary-title>Psychiatry Research-Neuroimaging</secondary-title></titles><periodical><full-title>Psychiatry Research-Neuroimaging</full-title></periodical><pages>182-190</pages><volume>193</volume><number>3</number><dates><year>2011</year><pub-dates><date>Sep</date></pub-dates></dates><isbn>0925-4927</isbn><accession-num>WOS:000295073400008</accession-num><urls><related-urls><url><Go to ISI>://WOS:000295073400008</url></related-urls></urls><electronic-resource-num>10.1016/j.pscychresns.2011.02.012</electronic-resource-num></record></Cite></EndNote>17, PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TbWl0aDwvQXV0aG9yPjxZZWFyPjIwMDc8L1llYXI+PFJl
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ADDIN EN.CITE.DATA 9, 19. For example, a study using the ADNI dataset reported a 0.35% mean difference in 385 subjects acquired in more than 50 sites (105 healthy controls, 195 subjects with MCI and 85 with AD) undergoing two SIENA measurements 12 months apart ADDIN EN.CITE <EndNote><Cite><Author>Cover</Author><Year>2011</Year><RecNum>16</RecNum><DisplayText><style face="superscript">17</style></DisplayText><record><rec-number>16</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">16</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Cover, K. S.</author><author>van Schijndel, R. A.</author><author>van Dijk, B. W.</author><author>Redolfi, A.</author><author>Knol, D. L.</author><author>Frisoni, G. B.</author><author>Barkhof, F.</author><author>Vrenken, H.</author><author>NeuGrid,</author><author>Alzheimer's Dis, Neuroimaging</author></authors></contributors><titles><title>Assessing the reproducibility of the SienaX and Siena brain atrophy measures using the ADNI back-to-back MP-RAGE MRI scans</title><secondary-title>Psychiatry Research-Neuroimaging</secondary-title></titles><periodical><full-title>Psychiatry Research-Neuroimaging</full-title></periodical><pages>182-190</pages><volume>193</volume><number>3</number><dates><year>2011</year><pub-dates><date>Sep</date></pub-dates></dates><isbn>0925-4927</isbn><accession-num>WOS:000295073400008</accession-num><urls><related-urls><url><Go to ISI>://WOS:000295073400008</url></related-urls></urls><electronic-resource-num>10.1016/j.pscychresns.2011.02.012</electronic-resource-num></record></Cite></EndNote>17. Further, the 90th percentile of the difference was much larger than expected (1.33%) for a Gaussian distribution. These results should be compared to the annualized mean percentage brain volume change obtained in that study: -0.65% ± 0.82% for healthy controls, -1.15% ± 1.21% for MCI, -1.84% ± 1.33% for AD.Between-centre reproducibility (effect of various vendors, magnetic field strengths, coils) Some studies have examined the between-centre reproducibility of atrophy measurements using SIENAPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Db3ZlcjwvQXV0aG9yPjxZZWFyPjIwMTE8L1llYXI+PFJl
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ADDIN EN.CITE.DATA 107. Similarly, other registration-based algorithms such as BSI show better reproducibility compared to segmentation-based techniques such as Freesurfer or SIENAXPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5EdXJhbmQtRHViaWVmPC9BdXRob3I+PFllYXI+MjAxMjwv
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ADDIN EN.CITE.DATA 22. However, segmentation-based methods using atlases can be improved by using fine registration methods and age-relevant templates ADDIN EN.CITE <EndNote><Cite><Author>Dickie</Author><Year>2015</Year><RecNum>176</RecNum><DisplayText><style face="superscript">108</style></DisplayText><record><rec-number>176</rec-number><foreign-keys><key app="EN" db-id="2pw5et9r4evrziezsznp9s0wwszxw50ttptr" timestamp="1444054987">176</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Dickie, D. A.</author><author>Job, D. E.</author><author>Gonzalez, D. R.</author><author>Shenkin, S. D.</author><author>Wardlaw, J. M.</author></authors></contributors><auth-address>Neuroimaging Sciences, Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh Medical School, Edinburgh, United Kingdom; Geriatric Medicine Unit, The University of Edinburgh, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom.
Geriatric Medicine Unit, The University of Edinburgh, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) collaboration, Glasgow, United Kingdom.</auth-address><titles><title>Use of brain MRI atlases to determine boundaries of age-related pathology: the importance of statistical method</title><secondary-title>PLoS One</secondary-title><alt-title>PloS one</alt-title></titles><periodical><full-title>PLoS One</full-title><abbr-1>PloS one</abbr-1></periodical><alt-periodical><full-title>PLoS One</full-title><abbr-1>PloS one</abbr-1></alt-periodical><pages>e0127939</pages><volume>10</volume><number>5</number><edition>2015/05/30</edition><dates><year>2015</year></dates><isbn>1932-6203</isbn><accession-num>26023913</accession-num><urls><related-urls><url>. Evaluation of coil effects on volumetric measurements showed that a change from a 12- to a 32-channel coil was responsible for a shift of 0.5% in the whole brain BSI ADDIN EN.CITE <EndNote><Cite><Author>Krueger</Author><Year>2012</Year><RecNum>17</RecNum><DisplayText><style face="superscript">100</style></DisplayText><record><rec-number>17</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">17</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Krueger, G.</author><author>Granziera, C.</author><author>Jack, C. R.</author><author>Gunter, J. L.</author><author>Littmann, A.</author><author>Mortamet, B.</author><author>Kannengiesser, S.</author><author>Sorensen, A. G.</author><author>Ward, C. P.</author><author>Reyes, D. A.</author><author>Britson, P. J.</author><author>Fischer, H.</author><author>Bernstein, M. A.</author></authors></contributors><titles><title>Effects of MRI scan acceleration on brain volume measurement consistency</title><secondary-title>Journal of Magnetic Resonance Imaging</secondary-title></titles><periodical><full-title>Journal of Magnetic Resonance Imaging</full-title></periodical><pages>1234-1240</pages><volume>36</volume><number>5</number><dates><year>2012</year><pub-dates><date>Nov</date></pub-dates></dates><isbn>1053-1807</isbn><accession-num>WOS:000310392800026</accession-num><urls><related-urls><url><Go to ISI>://WOS:000310392800026</url></related-urls></urls><electronic-resource-num>10.1002/jmri.23694</electronic-resource-num></record></Cite></EndNote>100, which appears unacceptable as it corresponds to the annual atrophy rate observed in healthy subjectsPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5PJmFwb3M7QnJpZW48L0F1dGhvcj48WWVhcj4yMDAxPC9Z
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ADDIN EN.CITE.DATA 109.Finally, analysing the back-to-back scans of the ADNI dataset in 200 pairs of images (118 subjects with 0-12 months pairs and 82 subjects with 6-24 months pairs), it has been reported than 3T was no more reproducible than 1.5T for the whole brain atrophy measure using SIENAPEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Db3ZlcjwvQXV0aG9yPjxZZWFyPjIwMTQ8L1llYXI+PFJl
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ADDIN EN.CITE.DATA 110.Contrast effectAtrophy measurement is strongly dependent on image contrast. Indeed, the error using BSI may exceed 100% if image contrast properties dramatically differ between the two scans in a measurement pair ADDIN EN.CITE <EndNote><Cite><Author>Preboske</Author><Year>2006</Year><RecNum>46</RecNum><DisplayText><style face="superscript">111</style></DisplayText><record><rec-number>46</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">46</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Preboske, G. M.</author><author>Gunter, J. L.</author><author>Ward, C. P.</author><author>Jack, C. R.</author></authors></contributors><auth-address>Mayo Clin & Mayo Fdn, Dept Radiol, Rochester, MN 55905 USA.
Jack, CR (reprint author), Mayo Clin & Mayo Fdn, Dept Radiol, 200 1st St SW, Rochester, MN 55905 USA.
jack.clifford@mayo.edu</auth-address><titles><title>Common MRI acquisition non-idealities significantly impact the output of the boundary shift integral method of measuring brain atrophy on serial MRI</title><secondary-title>Neuroimage</secondary-title></titles><periodical><full-title>Neuroimage</full-title></periodical><pages>1196-1202</pages><volume>30</volume><number>4</number><keywords><keyword>MRI</keyword><keyword>image processing</keyword><keyword>image artifacts</keyword><keyword>Alzheimer's disease</keyword><keyword>alzheimers-disease</keyword><keyword>entorhinal cortex</keyword><keyword>cerebral volume</keyword><keyword>rates</keyword><keyword>ad</keyword><keyword>progression</keyword><keyword>accurate</keyword><keyword>robust</keyword></keywords><dates><year>2006</year><pub-dates><date>May</date></pub-dates></dates><isbn>1053-8119</isbn><accession-num>WOS:000237601500013</accession-num><work-type>Article</work-type><urls><related-urls><url><Go to ISI>://WOS:000237601500013</url></related-urls></urls><electronic-resource-num>10.1016/j.neuroimage.2005.10.049</electronic-resource-num><language>English</language></record></Cite></EndNote>111. For example, in 10 volunteers, a mean brain volume change of -0.43% has been measured versus a +1.85% change when the second scan was acquired with a flip angle equal to 12° instead of 25° (the flip angle directly impacts the image contrast). Noise effectNoise is an unwanted random signal from various origins including: the subject’s body, coils, electronics, nearby machinery, etc. The signal-to-noise ratio is often computed as the ratio of the average signal intensity over the standard deviation of the noise. Signal-to-noise ratio directly impacts BSI values as, for example, the position of the brain surface is seen to retract as noise in the second time point scan increases ADDIN EN.CITE <EndNote><Cite><Author>Preboske</Author><Year>2006</Year><RecNum>46</RecNum><DisplayText><style face="superscript">111</style></DisplayText><record><rec-number>46</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">46</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Preboske, G. M.</author><author>Gunter, J. L.</author><author>Ward, C. P.</author><author>Jack, C. R.</author></authors></contributors><auth-address>Mayo Clin & Mayo Fdn, Dept Radiol, Rochester, MN 55905 USA.
Jack, CR (reprint author), Mayo Clin & Mayo Fdn, Dept Radiol, 200 1st St SW, Rochester, MN 55905 USA.
jack.clifford@mayo.edu</auth-address><titles><title>Common MRI acquisition non-idealities significantly impact the output of the boundary shift integral method of measuring brain atrophy on serial MRI</title><secondary-title>Neuroimage</secondary-title></titles><periodical><full-title>Neuroimage</full-title></periodical><pages>1196-1202</pages><volume>30</volume><number>4</number><keywords><keyword>MRI</keyword><keyword>image processing</keyword><keyword>image artifacts</keyword><keyword>Alzheimer's disease</keyword><keyword>alzheimers-disease</keyword><keyword>entorhinal cortex</keyword><keyword>cerebral volume</keyword><keyword>rates</keyword><keyword>ad</keyword><keyword>progression</keyword><keyword>accurate</keyword><keyword>robust</keyword></keywords><dates><year>2006</year><pub-dates><date>May</date></pub-dates></dates><isbn>1053-8119</isbn><accession-num>WOS:000237601500013</accession-num><work-type>Article</work-type><urls><related-urls><url><Go to ISI>://WOS:000237601500013</url></related-urls></urls><electronic-resource-num>10.1016/j.neuroimage.2005.10.049</electronic-resource-num><language>English</language></record></Cite></EndNote>111. Thus, the average percent volume change progressively diverges from zero with increasing noise mimicking increased brain atrophy.Subject-related factorsMotion will have an impact on atrophy measurement. For example, it has been reported that, as the amplitude of the motion in the second scan increases, the mean absolute brain and ventricular BSI brain volume changes as well as the associated standard deviation increase progressively ADDIN EN.CITE <EndNote><Cite><Author>Preboske</Author><Year>2006</Year><RecNum>46</RecNum><DisplayText><style face="superscript">111</style></DisplayText><record><rec-number>46</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">46</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Preboske, G. M.</author><author>Gunter, J. L.</author><author>Ward, C. P.</author><author>Jack, C. R.</author></authors></contributors><auth-address>Mayo Clin & Mayo Fdn, Dept Radiol, Rochester, MN 55905 USA.
Jack, CR (reprint author), Mayo Clin & Mayo Fdn, Dept Radiol, 200 1st St SW, Rochester, MN 55905 USA.
jack.clifford@mayo.edu</auth-address><titles><title>Common MRI acquisition non-idealities significantly impact the output of the boundary shift integral method of measuring brain atrophy on serial MRI</title><secondary-title>Neuroimage</secondary-title></titles><periodical><full-title>Neuroimage</full-title></periodical><pages>1196-1202</pages><volume>30</volume><number>4</number><keywords><keyword>MRI</keyword><keyword>image processing</keyword><keyword>image artifacts</keyword><keyword>Alzheimer's disease</keyword><keyword>alzheimers-disease</keyword><keyword>entorhinal cortex</keyword><keyword>cerebral volume</keyword><keyword>rates</keyword><keyword>ad</keyword><keyword>progression</keyword><keyword>accurate</keyword><keyword>robust</keyword></keywords><dates><year>2006</year><pub-dates><date>May</date></pub-dates></dates><isbn>1053-8119</isbn><accession-num>WOS:000237601500013</accession-num><work-type>Article</work-type><urls><related-urls><url><Go to ISI>://WOS:000237601500013</url></related-urls></urls><electronic-resource-num>10.1016/j.neuroimage.2005.10.049</electronic-resource-num><language>English</language></record></Cite></EndNote>111. However, the different levels of motion have been simulated in this latter study and the severity may have been overestimated.Subject’s head positioning in the magnet also influences brain atrophy measures. Indeed, nonlinear gradient distortions associated with shifts in the long magnet’s axis can significantly decrease the accuracy and precision of MRI-derived measures of brain atrophy assessed by SIENA ADDIN EN.CITE <EndNote><Cite><Author>Caramanos</Author><Year>2010</Year><RecNum>19</RecNum><DisplayText><style face="superscript">112</style></DisplayText><record><rec-number>19</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">19</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Caramanos, Z.</author><author>Fonov, V. S.</author><author>Francis, S. J.</author><author>Narayanan, S.</author><author>Pike, G. B.</author><author>Collins, D. L.</author><author>Arnold, D. L.</author></authors></contributors><titles><title>Gradient distortions in MRI: Characterizing and correcting for their effects on SIENA-generated measures of brain volume change</title><secondary-title>Neuroimage</secondary-title></titles><periodical><full-title>Neuroimage</full-title></periodical><pages>1601-1611</pages><volume>49</volume><number>2</number><dates><year>2010</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>1053-8119</isbn><accession-num>WOS:000272808400043</accession-num><urls><related-urls><url><Go to ISI>://WOS:000272808400043</url></related-urls></urls><electronic-resource-num>10.1016/j.neuroimage.2009.08.008</electronic-resource-num></record></Cite></EndNote>112. These negative effects increase in magnitude with: (i) increases in the Z-distance (long magnet’s axis) between the brains to be compared at two time points; and, (ii) increases in the Z-distance from magnet isocenter to the center of the pair of brains to be compared ADDIN EN.CITE <EndNote><Cite><Author>Caramanos</Author><Year>2010</Year><RecNum>19</RecNum><DisplayText><style face="superscript">112</style></DisplayText><record><rec-number>19</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">19</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Caramanos, Z.</author><author>Fonov, V. S.</author><author>Francis, S. J.</author><author>Narayanan, S.</author><author>Pike, G. B.</author><author>Collins, D. L.</author><author>Arnold, D. L.</author></authors></contributors><titles><title>Gradient distortions in MRI: Characterizing and correcting for their effects on SIENA-generated measures of brain volume change</title><secondary-title>Neuroimage</secondary-title></titles><periodical><full-title>Neuroimage</full-title></periodical><pages>1601-1611</pages><volume>49</volume><number>2</number><dates><year>2010</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>1053-8119</isbn><accession-num>WOS:000272808400043</accession-num><urls><related-urls><url><Go to ISI>://WOS:000272808400043</url></related-urls></urls><electronic-resource-num>10.1016/j.neuroimage.2009.08.008</electronic-resource-num></record></Cite></EndNote>112. Typical gradient distortion field for a T1-weighted acquisition is presented in Fig. 4. It shows the dramatic shifting experienced by voxels especially at the border of the brain and the need for accurate correction and repositioning of patients in longitudinal studies. Techniques to manage variabilityWe have highlighted a large number of reasons why quantitative MRI markers of SVD may be variable beyond subject-related or disease-related factors. These sources of variability introduce significant barriers, and potential biases, to multicentre imaging studies of SVD. Fortunately, while adding variability to the measurements of markers is inherent to imaging studies, some techniques have been designed to reduce it. First, an optimised MRI protocol with high contrast-to-noise ratio, minimal artefacts and good reproducibility must be determined. Hereto, a methodological approach has been proposed to target a protocol with the highest possible reproducibility based on the assessment of quality measures ADDIN EN.CITE <EndNote><Cite><Author>Chalavi</Author><Year>2012</Year><RecNum>82</RecNum><DisplayText><style face="superscript">21</style></DisplayText><record><rec-number>82</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">82</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Chalavi, S.</author><author>Simmons, A.</author><author>Dijkstra, H.</author><author>Barker, G. J.</author><author>Reinders, Aats</author></authors></contributors><titles><title>Quantitative and qualitative assessment of structural magnetic resonance imaging data in a two-center study</title><secondary-title>Bmc Medical Imaging</secondary-title></titles><periodical><full-title>Bmc Medical Imaging</full-title></periodical><volume>12</volume><dates><year>2012</year><pub-dates><date>Aug</date></pub-dates></dates><isbn>1471-2342</isbn><accession-num>WOS:000321352700001</accession-num><urls><related-urls><url><Go to ISI>://WOS:000321352700001</url></related-urls></urls><electronic-resource-num>10.1186/1471-2342-12-27</electronic-resource-num></record></Cite></EndNote>21. The principle is to acquire several datasets including: within-subject within-centre datasets, between-subjects between-centre datasets and between-subject within-centre datasets. By analysing the summary data quality and quantitative measures extracted with dedicated and validated software the desired optimal MRI protocol can be determined.Second, to account for the problem of upgrades in longitudinal studies, Jovicich et al. advise planning a ‘system upgrade calibration study’ as part of the design, with subjects scanned shortly prior to and immediately after the upgrade so as to derive a correct estimation for potential systematic biases ADDIN EN.CITE <EndNote><Cite><Author>Jovicich</Author><Year>2009</Year><RecNum>12</RecNum><DisplayText><style face="superscript">10</style></DisplayText><record><rec-number>12</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">12</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Jovicich, J.</author><author>Czanner, S.</author><author>Han, X.</author><author>Salat, D.</author><author>van der Kouwe, A.</author><author>Quinn, B.</author><author>Pacheco, J.</author><author>Albert, M.</author><author>Killiany, R.</author><author>Blacker, D.</author><author>Maguire, P.</author><author>Rosas, D.</author><author>Makris, N.</author><author>Gollub, R.</author><author>Dale, A.</author><author>Dickerson, B. C.</author><author>Fischl, B.</author></authors></contributors><titles><title>MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: Reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths</title><secondary-title>Neuroimage</secondary-title></titles><periodical><full-title>Neuroimage</full-title></periodical><pages>177-192</pages><volume>46</volume><number>1</number><dates><year>2009</year><pub-dates><date>May</date></pub-dates></dates><isbn>1053-8119</isbn><accession-num>WOS:000265723800020</accession-num><urls><related-urls><url><Go to ISI>://WOS:000265723800020</url><url>, several corrective techniques based on the MR system characteristics PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Kb3ZpY2ljaDwvQXV0aG9yPjxZZWFyPjIwMDY8L1llYXI+
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ADDIN EN.CITE.DATA 112, 115, 116 have been developed to reduce the effects of gradient distortion on imaging reproducibility. The advantage of using a phantom travelling from one site to another is to apply a unified correction independently of the vendor’s own corrections. Correction for gradient nonlinearity and intensity non-uniformity reduces the variance of longitudinal changes in brain volumes and improves the accuracy for detecting subtle volumetric changes ADDIN EN.CITE <EndNote><Cite><Author>Takao</Author><Year>2010</Year><RecNum>86</RecNum><DisplayText><style face="superscript">117</style></DisplayText><record><rec-number>86</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">86</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Takao, H.</author><author>Abe, O.</author><author>Hayashi, N.</author><author>Kabasawa, H.</author><author>Ohtomo, K.</author></authors></contributors><titles><title>Effects of Gradient Non-Linearity Correction and Intensity Non-Uniformity Correction in Longitudinal Studies Using Structural Image Evaluation Using Normalization of Atrophy (SIENA)</title><secondary-title>Journal of Magnetic Resonance Imaging</secondary-title></titles><periodical><full-title>Journal of Magnetic Resonance Imaging</full-title></periodical><pages>489-492</pages><volume>32</volume><number>2</number><dates><year>2010</year><pub-dates><date>Aug</date></pub-dates></dates><isbn>1053-1807</isbn><accession-num>WOS:000280447300034</accession-num><urls><related-urls><url><Go to ISI>://WOS:000280447300034</url></related-urls></urls><electronic-resource-num>10.1002/jmri.22237</electronic-resource-num></record></Cite></EndNote>117. For example, in 208 subjects, the raw mean percentage of brain volume change was -0.59% against -0.46% when images were corrected for gradient nonlinearity and intensity non-uniformity ADDIN EN.CITE <EndNote><Cite><Author>Takao</Author><Year>2010</Year><RecNum>86</RecNum><DisplayText><style face="superscript">117</style></DisplayText><record><rec-number>86</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">86</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Takao, H.</author><author>Abe, O.</author><author>Hayashi, N.</author><author>Kabasawa, H.</author><author>Ohtomo, K.</author></authors></contributors><titles><title>Effects of Gradient Non-Linearity Correction and Intensity Non-Uniformity Correction in Longitudinal Studies Using Structural Image Evaluation Using Normalization of Atrophy (SIENA)</title><secondary-title>Journal of Magnetic Resonance Imaging</secondary-title></titles><periodical><full-title>Journal of Magnetic Resonance Imaging</full-title></periodical><pages>489-492</pages><volume>32</volume><number>2</number><dates><year>2010</year><pub-dates><date>Aug</date></pub-dates></dates><isbn>1053-1807</isbn><accession-num>WOS:000280447300034</accession-num><urls><related-urls><url><Go to ISI>://WOS:000280447300034</url></related-urls></urls><electronic-resource-num>10.1002/jmri.22237</electronic-resource-num></record></Cite></EndNote>117. However, if the expected group difference far exceeds brain volume change related to geometric distortions, the extra time and expense associated to the phantom-based correction needs to be carefully evaluated.Finally, the use of dedicated statistical methods is important in analysing multicentre MRI data. A simple a posteriori method to account for scanner hardware variability (combination of different devices, coil types and field strengths) is to include the scanner hardware as a covariate in regression models ADDIN EN.CITE <EndNote><Cite><Author>Kruggel</Author><Year>2010</Year><RecNum>11</RecNum><DisplayText><style face="superscript">14</style></DisplayText><record><rec-number>11</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">11</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Kruggel, F.</author><author>Turner, J.</author><author>Muftuler, L. T.</author><author>Alzheimers Dis Neuroimaging, Initia</author></authors></contributors><titles><title>Impact of scanner hardware and imaging protocol on image quality and compartment volume precision in the ADNI cohort</title><secondary-title>Neuroimage</secondary-title></titles><periodical><full-title>Neuroimage</full-title></periodical><pages>2123-2133</pages><volume>49</volume><number>3</number><dates><year>2010</year><pub-dates><date>Feb</date></pub-dates></dates><isbn>1053-8119</isbn><accession-num>WOS:000273626400019</accession-num><urls><related-urls><url><Go to ISI>://WOS:000273626400019</url></related-urls></urls><electronic-resource-num>10.1016/j.neuroimage.2009.11.006</electronic-resource-num></record></Cite></EndNote>14. Moreover, it has been shown that T1-weighted regional anatomical brain volume data can be reliably combined across 1.5T and 3T centres with the application of an appropriate correction procedure (regression-based correction function when data varied in a linear and systematic fashion) ADDIN EN.CITE <EndNote><Cite><Author>Pfefferbaum</Author><Year>2012</Year><RecNum>83</RecNum><DisplayText><style face="superscript">118</style></DisplayText><record><rec-number>83</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">83</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Pfefferbaum, A.</author><author>Rohlfing, T.</author><author>Rosenbloom, M. J.</author><author>Sullivan, E. V.</author></authors></contributors><titles><title>Combining atlas-based parcellation of regional brain data acquired across scanners at 1.5 T and 3.0 T field strengths</title><secondary-title>Neuroimage</secondary-title></titles><periodical><full-title>Neuroimage</full-title></periodical><pages>940-951</pages><volume>60</volume><number>2</number><dates><year>2012</year><pub-dates><date>Apr</date></pub-dates></dates><isbn>1053-8119</isbn><accession-num>WOS:000303272300012</accession-num><urls><related-urls><url><Go to ISI>://WOS:000303272300012</url></related-urls></urls><electronic-resource-num>10.1016/j.neuroimage.2012.01.092</electronic-resource-num></record></Cite></EndNote>118. More sophisticated statistical methods such as linear mixed-effect models including protocol as well as individual MRI acquisition parameters have also emerged to more accurately model longitudinal changes (BPF and volume of WMH) PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5DaHVhPC9BdXRob3I+PFllYXI+MjAxNTwvWWVhcj48UmVj
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ADDIN EN.CITE.DATA 94, 122 and image processing on raw data, using consistent techniques, can reduce the variability introduced by a posteriori image processing across centres. 3.4 Suggestions for future researchA between-centre variability study in a large group of subjects, representing a range of types and burdens of SVD lesions, to assess the effects of vendor and field strength (protocols as close as possible) on reproducibility of SVD lesions and brain volumes is missing. So are experiments examining the effects of changing coil, correcting for gradient distortion or field heterogeneities, upgrading the system, or varying classical parameters such as TE, TI or TR, on the final WMH volume. Finally, WMH appears to be a reliable marker in single centre studies where all acquisition and processing parameters are kept constant, but additional studies are missing to infer the true reproducibility of WMH measures in a multicentre context.As for WMH, a multicentre study assessing the effect of scanner hardware (same magnetic field but different vendors and models) on CMB detection with a given optimized protocol would greatly add to the knowledge on the reliability of CMB detection. New data on methods of automated detection of CMB throughout different brain regions are needed. Also, the analysis of the potential effects of coil, and voxel size would be particularly useful. Finally, CMB can be used as a reliable marker in single centre studies but additional efforts are needed to determine the sources of variability in multicentre data.For lacunes and PVS, more automated and validated image processing algorithms are required, followed by assessment of reproducibility through both within and between centres acquisitions. In view of the likely interactions between acquisition protocols and performance of analysis methods, the assessment of all SVD features should be included in any study that assesses reliability of WMH and/or CMB and/or atrophy quantification. Ten key points for assessing quantitative SVD markers in multicentre studiesBased on our review of the current literature regarding reproducibility and acquisition-related sources of variability of MRI markers in SVD and on personal experiences, we propose the following key points should be considered when designing or analysing a multicentre MRI study in SVD. Obviously, prior to acquisitions, the scientific question should guide which marker is the focus, although opportunities to assess all SVD markers should be obtained where possible. Optimising acquisition for contrast, signal-to-noise ratio and spatial resolution is particularly important when imaging elderly or cognitively impaired patient populations who will not tolerate prolonged acquisition times. Importantly, although it is essential to have accurate and reproducible measurements, the nature of the questions will be guiding the level of precision that is needed. Requirements might be different between interventional clinical trials with relatively short term duration and long-term population-based observational studies. For example, in some situations, the added noise due to multicentre variability will not alter a strong statistical association between one SVD-related MRI marker and one clinical outcome. Conversely, studying the quantitative evolution of one SVD-related marker during a short follow-up will require a very fine quantification that necessitates a variability caused by technological factors as small as possible. Hence we organized our recommendations as being a) advisable whatever the question guiding the study as they tackle effects being important and/or involve easy-to-follow techniques to manage variability, b) advisable in some conditions, i.e. when the expected effect of interest is small relatively to the unwanted noise caused by the multicentre setting. a) strongly advisable and easy-to-follow recommendationsAutomated quality checks for MRI sequence parameters are advisable (for each scan, comparison of the acquisition parameters with the intended protocol for each scanner). Also, images should be monitored for motion and other artefacts such as magnetic susceptibility artefacts, aliasing artefacts or Gibbs artefacts. A minimum standard for quality control will have to be defined to exclude problematic data (for example, based on the severity of artefacts or from automatic measures of signal to noise ratio, background noise, etc.). Indeed, even if the computational method seems to run properly, artefacts can dramatically alter the data.MRI gradient nonlinearities have to be corrected at least with corrections proposed in options by the manufacturer as they can affect dramatically measures of brain atrophy.Subject’s positioning in the scanner is another key aspect and strict instructions for MR technicians may be particularly useful to reproduce confidently the position of the head, from one subject to another, and from the first time point to the following time points, in longitudinal studies.Considering the hardware, the same coil has to be used during an MR protocol both in cross-sectional and longitudinal studies. When upgrades or repairs are needed during the study period, a strategy should be in place to scan some participants twice, before and after the upgrade, to estimate the potential bias for a posteriori analyses. Otherwise, the imaging protocol and hardware should be kept as constant as possible over the whole time of the study.b) recommendations to evaluate given the targeted trade-off between sensitivity and variabilityWithin-centre reproducibility is much higher than between-centre reproducibility. The need for a multicentre setup should then be carefully evaluated while taking into account specific aspects such as number of participants, subject transportation, data centralization, quality control and cost. For multicentre studies, the use of only one vendor or only some models can be an option.In multicentre studies, protocol harmonization is a mandatory step to obtain similar sequences with acquisition parameters as close as possible. There is no way to get a perfect harmonization of MRI protocols between different vendors and models. Therefore, it is a trade-off between a high reproducibility of markers (few centres, same vendors) and a sufficient statistical power through the recruitment of a large number of subjects, generally in several centres with different scanner vendors and models. Specifying acceptable ranges for different acquisition parameters may help obtain satisfactory harmonization for large-scale clinical studies.Spatial resolution is a key parameter in assessing correctly the variation of any MRI marker such as brain volumetry, longitudinal atrophy, volume of WMH or quantification of lacunes and PVS. Increasing spatial resolution decreases signal-to-noise ratio and increases acquisition duration. Sensitivity for quantifying small objects such as lacunes and PVS will be compromised if using voxels as big as the object itself or where there is a slice gap. Selection of optimal spatial resolution must therefore be targeted to the primary question.When analysing multicentre MRI data, the use of statistical models to take into account the induced variability is recommended (adjusting on centre or using random effects models for example).While some quantitative MRI markers of SVD are regularly assessed (brain atrophy, WMH volume), others are difficult to quantify given their small size in relation to conventional anatomical MRI (lacunes, PVS) or their strong dependence on MRI parameters (CMB). High-field MRI and detailed studies of these particular markers may be performed in studies nested within larger studies assessing more common markers in a clinical setting. For CMB characterization, SWI or quantitative susceptibility mapping are promising techniques. 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ADDIN EN.CITE.DATA 79.It is important for studies to acknowledge that variation is inevitable and they should detail the steps that have been made to minimise this variation.ConclusionThere is a need to reduce image variability induced by technological choices (coil, MR sequence, MR parameters and options) as much as possible in large multicentre research studies and clinical trials using MRI in SVD. Indeed, the added variance due to the multicentre setup can be deleterious in various applications and should be minimised using multiple approaches. Protocol harmonization and a posteriori corrections can help in minimizing biases; minimisation on centre or scanner type may also be advisable. Even if several sources of variability have been previously evaluated, most studies have focused on brain volume measurements while few have focused on WMH quantification. Further, less is known concerning the sources of variability when quantifying lacunes, CMB and PVS. Additional studies are warranted to investigate the variability of these makers, which is a key aspect for future longitudinal or multicentre studies. Other potential SVD-related MRI markers derived from new and multiple techniques (such as diffusion tensor imaging, arterial spin labelling, magnetization transfer mapping, blood-brain barrier permeability imaging, BOLD functional MRI, cortical morphology, cortical microinfarcts, cortical superficial siderosis) will also have to address these important questions regarding their reproducibility to facilitate large-scale, multicentre research.Author contributionFDG, EJ and HC designed the review. FDG collected and analysed the literature. FDG, EJ and HC wrote the initial draft of the review. JMW critically revised the manuscript. The manuscript was then sent to the imaging and small vessel disease experts involved in the COEN group (network of Centres of Excellence in Neurodegeneration, ). All authors of the present manuscript agreed to contribute and revised carefully the manuscript. Finally, all authors accepted the current version.Disclosure/conflict of interestThe authors declare no conflict of interest.Supplementary material for this paper can be found at HYPERLINK "" \t "_blank" ADDIN EN.REFLIST 1.Román GC, Erkinjuntti T, Wallin A, Pantoni L, Chui HC. Subcortical ischaemic vascular dementia. Lancet Neurology 2002; 1(7): 426-436.2.Chabriat H, Joutel A, Dichgans M, Tournier-Lasserve E, Bousser M-G. Cadasil. Lancet Neurology 2009; 8(7): 643-653.3.Selnes OA, Vinters HV. Vascular cognitive impairment. Nature Clinical Practice Neurology 2006; 2(10): 538-547.4.Pantoni L. Cerebral small vessel disease: from pathogenesis and clinical characteristics to therapeutic challenges. Lancet neurology 2010; 9(7): 689-701.5.Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R et al. 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ADDIN EN.CITE <EndNote><Cite><Author>Polak</Author><Year>2012</Year><RecNum>37</RecNum><DisplayText><style face="superscript">42</style></DisplayText><record><rec-number>37</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">37</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Polak, P.</author><author>Magnano, C.</author><author>Zivadinov, R.</author><author>Poloni, G.</author></authors></contributors><titles><title>3D FLAIRED: 3D fluid attenuated inversion recovery for enhanced detection of lesions in multiple sclerosis</title><secondary-title>Magnetic Resonance in Medicine</secondary-title></titles><periodical><full-title>Magnetic Resonance in Medicine</full-title></periodical><pages>874-881</pages><volume>68</volume><number>3</number><dates><year>2012</year><pub-dates><date>Sep</date></pub-dates></dates><isbn>0740-3194</isbn><accession-num>WOS:000308098100025</accession-num><urls><related-urls><url><Go to ISI>://WOS:000308098100025</url></related-urls></urls><electronic-resource-num>10.1002/mrm.23289</electronic-resource-num></record></Cite></EndNote>42)Figure 2 legend: Sagittal slices from (a, d) 3D FLAIRED (a FLAIR sequence optimized in order to enhance detection of lesions); (b, e) GE 3D FLAIR; and (c, f) 2D FLAIR. 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ADDIN EN.CITE.DATA 28)Figure 3 legend: The pairs of images illustrate comparisons of thick-section GRE (A) versus thin-section SWI (B); thin-section GRE (C) versus thin-section SWI (D); thick-section GRE (E) versus thin-section GRE (F) (all preceding images at 1.5T); and SWI at 1.5T (G) versus SWI at 3T (H). The black arrows in (A) and (B) illustrate a CMB prospectively counted on both sequences, whereas lesions denoted by white arrows were initially identified only on the SWI image. The black arrows in the remaining images highlight lesions on the paired images for comparison.Figure 4 title: Typical gradient distortion field in a T1-weighted acquisition (courtesy of Caramanos et al. ADDIN EN.CITE <EndNote><Cite><Author>Caramanos</Author><Year>2010</Year><RecNum>19</RecNum><DisplayText><style face="superscript">112</style></DisplayText><record><rec-number>19</rec-number><foreign-keys><key app="EN" db-id="2ewzz9dwq5rv9qe2ee8pwvdaarr0ew0reaes">19</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Caramanos, Z.</author><author>Fonov, V. S.</author><author>Francis, S. J.</author><author>Narayanan, S.</author><author>Pike, G. B.</author><author>Collins, D. L.</author><author>Arnold, D. L.</author></authors></contributors><titles><title>Gradient distortions in MRI: Characterizing and correcting for their effects on SIENA-generated measures of brain volume change</title><secondary-title>Neuroimage</secondary-title></titles><periodical><full-title>Neuroimage</full-title></periodical><pages>1601-1611</pages><volume>49</volume><number>2</number><dates><year>2010</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>1053-8119</isbn><accession-num>WOS:000272808400043</accession-num><urls><related-urls><url><Go to ISI>://WOS:000272808400043</url></related-urls></urls><electronic-resource-num>10.1016/j.neuroimage.2009.08.008</electronic-resource-num></record></Cite></EndNote>112)Figure 4 legend: Sagittal, coronal, and axial views of a phantom-based gradient distortion field that was determined based on a T1-weighted acquisition. The color scale represents the distance (in millimeters) that a voxel moves because of gradient distortion between its “real” location and its “apparent” location on such an MRI scan that has not been corrected for the gradient distortion.Table 1. Summary of reproducibility studies (within-centre or/and between centre scan-rescan procedure)StudyMarker of interestBetween centreWithin centreMagnetic fieldAnalysis toolBetween-centre reproducibilityWithin-centre reproducibilityFirst authorYear publishedNb subjects rescannedNb of MRI sitesNb subjects rescannedNb rescans per subjectBrain volumetrySchnack2004ICV, BV, GMV, WMV64521.5T, 1Town segmentationBV: ICC = 0.98BV: ICC = 1Ewers2006GMV, WMV, CSFV110NANA1.5TSPM2CoV=5% for all volumesNAJovicich2009ICV1531521.5T, 3TFreesurferNAReproducibility error=2.56%Reig2009ICV, GMV, WMV, CSFV55NANA1.5TSPM-basedICV: CoV=1.9%, ICC=0.99NAWonderlick2009BV, GMV, WMV112NANA3TFreesurferBV: ICC=0.99NADe Boer2010BV, GMV, WMV, CSFVNANA3021.5TFSL, SPM5, kNNNABV: CoV=0.39% with FSL, CoV=0.72% with SPM5Huppertz2010BV, GMV, WMV, CSFV, ICV16131.5T, 3TSPM5BV: CoV=3.78%; ICV: CoV=2.97%BV: CoV=0.5%; ICV: CoV=0.6%Kruggel2010ICV, BV, GMV, WMV172594121.5T, 3TFantasmBV: median RAVD=4.76%, 90th percentile=12.34%; ICV: median RAVD=1.66%, 90th percentile=3.92%BV: median RAVD=0.30%, 90th percentile RAVD=1.09%; ICV: median RAVD=0.29%, 90th percentile RAVD=1.27%Sampat2010BPFNANA1821.5Town segmentationNABPF: CoV=0.46%Cover2011Normalized brain volumeNANA67121.5TSIENAXNABV: median RAVD=0.96%, 90th percentile RAVD=5.11%Shokouhi (1.5T)2011ICV, GMV, WMV, CSFV1331321.5TBrainVISABV: reliabilitya=0.97BV: reliability=0.99Shokouhi (3T)2011ICV, GMV, WMV, CSFV1151123TBrainVISABV: reliability=0.88BV: reliability=0.96Landman2011GMW, WMVNANA2123TJava Imaging Science ToolkitNAGMV: CoV=2.6%; WMV: CoV=1.5%Chalavi2012GMW, WMV32323TFreesurfer, SPMNANAHuang2012WMV, CSFV62NANA3TFSLWMV: ICC=0.62; CSFV: ICC=0.87NAPfefferbaum2012Supratentorial volume1142NANA1.5T, 3TFreesurferBV: ICC=0.81NABrain atrophySmith2007PBVCNANA6821.5TSIENA, BSINASIENA: median RAVD=0.16%; BSI: median RAVD=0.17%De Bresser2011PBVCNANA1021.5TSIENA, SPM8, kNNNAcoefficient of repeatabilityb=0.92%for SIENA, 2.64% for SPM8, 0.31% for kNNCover2011PBVCNANA38521.5TSIENANAPBVC: median RAVD=0.35%, 90th percentile RAVD=1.37%Durand-dubief2012Atrophy92NANA1.5T7 segmentation algorithmsSEM: 1.77 for Freesurfer, 0.98 for BSI, 0.85 for SIENANAWhite-matter hypenintensitiesWei2002WMH volumeNANA2021.5T3 pipelines: PVEC, TDS, TDS+NACoV range: 2.6 - 7.5% according to pipelineSicotte2003WMH volume252NANA1.5T, 3Tthreshold-based segmentationmedian RAVD: 12.5%NADe Boer2010WMH volumeNANA3021.5Tauto trained kNNNACoV: 5.87%Theysohn2011WMH grading202NANA1.5T, 7T75% of subjects with equal grading at 7T compared to 1.5T, 13% superior and 13% inferiorNARamirez2013WMH volume (total subcortical, deep white, periventricular)NANA2021.5TLesion ExplorerNAICC=1; mean AVD in 10 older subjects=213mm3Cerebral microbleedsStehling2008CMB number252NANA1.5T, 3Tmore CMB at 3T vs 1.5TNANandigam2009CMB contrast index and diameter42NANA1.5T, 3Tsmaller contrast index and diameter at 1.5T vs 3TNAConijn2011CMB number342NANA1.5T, 7Tmore CMB at 7T vs 1.5TNATheysohn2011CMB number202NANA1.5T, 7Tmore CMB at 7T vs 1.5TNAICV: intracranial volume, BV: brain volume (GMV + WMV), GMV: gray matter volume, WMV: white matter volume, CSFV: cerebrospinal volume, BPF: Brain parenchymal fraction, WMH: white matter hyperintensities, CMB: cerebral microbleeds, ICC: Intraclass correlation coefficient, CoV: coefficient of variation, NA: not available, Reproducibility error: The group reproducibility error for each structure is derived averaging the reproducibility errors across subjects, where for each subject the error is estimated as the absolute test–retest volume percent change relative to the mean test–retest volume, kNN: k-Nearest-Neighbors segmentation method, RAVD: relative absolute value of the difference, PBVC: percent brain volume change, areliability in this study has been computed as the ratio of the variance excluding the contribution from the factor (within-centre or between-centre) to the total variance, bcoefficient of repeatability: 1.96 times the standard deviation of the differences between two measurements (Bland-Altman), SEM: standard error of measurement, AVD: absolute value of the difference. 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