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Repeatability of three R2′-based algorithms (χ-Separation, χ-SepNet, and APART) was evaluated using 3-T scan–rescan data from 21 healthy subjects, using ICC and repeatability coefficient (RC). Results varied by method and brain region: APART and χ-SepNet performed best, with lower repeatability near frontal lobes and air–tissue interfaces. Overall reliability was moderate to good, constrained mainly by the R2′ input, and algorithm choice strongly influenced susceptibility values.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/repeatability-of-susceptibility-source-separation-methods-in-human-brain-a-single-site-study-at-3-t/440484/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/repeatability-of-susceptibility-source-separation-methods-in-human-brain-a-single-site-study-at-3-t/440484.png","ImageObject",300,407,{"name":92,"@type":93},"jayni","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-30","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does susceptibility source separation address compared with conventional QSM?","Question",{"text":112,"@type":113},"Conventional QSM is sensitive mainly to the net effect of iron and myelin mixtures within a voxel. Susceptibility source separation estimates paramagnetic and diamagnetic contributions more directly, enabling subvoxel insights.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which algorithms were evaluated for repeatability in this study?",{"text":117,"@type":113},"The study investigated three R2′-based algorithms: χ-Separation, χ-SepNet, and APART.",{"name":119,"@type":110,"acceptedAnswer":120},"How was repeatability assessed, and how did performance vary by region?",{"text":121,"@type":113},"Repeatability was measured using intraclass correlation coefficient (ICC) and repeatability coefficient (RC). Repeatability differed across methods and regions, with lower reliability in the frontal lobe and near air–tissue interfaces.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},440484,1790792060,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":14,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":41},3985747859343,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","NMR in Biomedicine  \nRESEARCH ARTICLE  OPEN ACCESS   \nRepeatability of Susceptibility Source Separation Methods in Human Brain: A Single-Site Study at 3 T  \nNashwan Naji1  | Peter Seres1 | Gerald Moran2 | Christian Beaulieu1,3 | Alan H. Wilman1,3  \n1Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada | 2Siemens Healthcare Limited, Oakville, Ontario,  \nCanada | 3Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada Correspondence: Nashwan Naji ([nashwana@ualberta.ca](nashwana@ualberta.ca))  \nReceived: 27 June 2025 | Revised: 14 December 2025 | Accepted: 23 December 2025  \nKeywords: 3 T | repeatability | scan–rescan | susceptibility mapping | susceptibility source separation  \nABSTRACT  \nSusceptibility source separation offers new subvoxel insights into iron and myelin distribution in the human brain, overcoming a known limitation of conventional quantitative susceptibility mapping (QSM), which is sensitive only to the net effect of theiron and myelin mixture. Several algorithms have been developed to perform susceptibility separation; however, their repeatability has not been thoroughly evaluated. The repeatability of three R2′-based algorithms (χ-Separation, χ-SepNet, and APART) was investigated using 3-T scan–rescan data from 21 healthy subjects. Repeatability was assessed using intraclass correlation coefficient (ICC) and repeatability coefficient (RC). Additionally, the average value of the generated maps was used to evaluate the contrast produced by different algorithms. Results showed that repeatability varied between methods and across regions, with better performance achieved by APART and χ-SepNet. The obtained paramagnetic (χ+) and diamagnetic (χ−) maps had an overall moderate to good reliability, lower than that of conventional QSM primarily because of the lower reliability of the R2′ input. Region-wise, repeatability was lower in the frontal lobe and near air–tissue interfaces. The average RC for APART was 4 ppb, except for iron-rich regions on χ+ maps, where it was 7 ppb. For χ-SepNet, the average RC was 5 ppb and 10 ppb for χ+ in iron-rich regions. APART maps had the lowest average susceptibility values, but this was highly dependent on the method used to calculate the initial QSM input. In conclusion, susceptibility source separation showed moderate to good repeatability in most brain regions; however, results were greatly influenced by the algorithm used.  \n1 | Introduction  \nIron and myelin are key players in brain function and measuring their abnormalities and evolution over time is of particular interest in studying neurodegenerative diseases, such as  \nmultiple sclerosis, Alzheimer's disease, and Parkinson's disease [1–6] . Iron and myelin are the dominant sources of magnetic susceptibility in the brain [7] and can be measured using a noninvasive MR technique known as quantitative susceptibility mapping (QSM) produced from phase images obtained using  \n\n| Abbreviations: ant, anterior; ANTs, advanced normalization tools; APART, iterative magnetic susceptibility sources separation; AV, average value; CC, corpus callosum; CP, cerebellar peduncle; CR, corona radiata; CRP, cerebral peduncle; CST, corticospinal tract; EC, external capsule; FNIRT, FMRIB's nonlinear image registration tool; FSL, FMRIB Software Library; GRAPPA, generalized autocalibrating partial parallel acquisition; GRE, gradient recalled echo; IC, internal capsule; ICC, intraclass correlation coefficient; JHU, Johns Hopkins University; LOA, limits of agreement; MEDI, morphology-enabled dipole inversion; MEGE, multiecho gradient echo; ML, medial lemniscus; MNI152, Montreal Neurological Institute 152-subject average brain template; PCT, pontine crossing tract; post, posterior; ppb, part per billion; ppm, part per million; QSM, quantitative susceptibility mapping; R2, irreversible transverse relaxation rate; R2*, effective transverse relaxation rate; R2′, reversible transver","cbCaikj57tMplYXe","https://ap.wps.com/l/cbCaikj57tMplYXe","pdf",26735020,12,"English","# Introduction\n## Susceptibility source separation and R2′-based approaches\n# Methods and metrics\n## Algorithms evaluated and scan–rescan design\n## Reliability assessment using ICC and RC\n# Results\n## Repeatability across methods and brain regions\n## RC values for APART and χ-SepNet\n# Discussion and conclusion\n## Dependence on algorithm and R2′ reliability","[{\"question\":\"What problem does susceptibility source separation address compared with conventional QSM?\",\"answer\":\"Conventional QSM is sensitive mainly to the net effect of iron and myelin mixtures within a voxel. Susceptibility source separation estimates paramagnetic and diamagnetic contributions more directly, enabling subvoxel insights.\"},{\"question\":\"Which algorithms were evaluated for repeatability in this study?\",\"answer\":\"The study investigated three R2′-based algorithms: χ-Separation, χ-SepNet, and APART.\"},{\"question\":\"How was repeatability assessed, and how did performance vary by region?\",\"answer\":\"Repeatability was measured using intraclass correlation coefficient (ICC) and repeatability coefficient (RC). Repeatability differed across methods and regions, with lower reliability in the frontal lobe and near air–tissue interfaces.\"}]","Repeatability of Susceptibility Source Separation Methods in Human Brain: A Single-Site Study at 3 T | PDF",1790692363]