Abstract<\/h2>\n\nPurpose<\/h3>\n
The diffusion MRI<\/a> \u201cb-vector\u201d table describing the diffusion<\/a> sensitization direction can be flipped and permuted in dimension due to different orientation conventions used in scanners and incorrect or improperly utilized file formats. This can lead to incorrect fiber orientation estimates and subsequent tractography<\/a> failure. Here, we present an automated quality control procedure to detect when the b-table is flipped and\/or permuted incorrectly.<\/p>\n<\/div>\n\nMethods<\/h3>\n
We define a \u201cfiber coherence index\u201d to describe how well fibers are connected to each other, and use it to automatically detect the correct configuration of b-vectors. We examined the performance on 3981 research subject scans (Baltimore Longitudinal Study of Aging), 1065 normal subject scans of high image quality (Human Connectome<\/a> Project), and 202 patient scans (Vanderbilt University Medical Center), as well as 9 in-vivo and 9 ex-vivo<\/a> animal data.<\/p>\n<\/div>\n\nResults<\/h3>\n
The coherence index resulted in a 99.9% (3979\/3981) and 100% (1065\/1065) success rate in normal subject scans, 98% (198\/202) in patient scans, and 100% (18\/18) in both in-vivo and ex-vivo animal data in detecting the correct gradient table in datasets without severe image artifacts. The four failing cases (4\/202) in patient scans, and two failures in healthy subject scans (2\/3981), all showed prominent motion or signal dropout artifacts.<\/p>\n<\/div>\n
\nConclusions<\/h3>\n
The fiber coherence measure can be used as an automatic quality assurance check in any diffusion analysis pipeline. Additionally, the success of this fiber coherence measure suggests potential broader applications, including evaluating data quality, or even providing diagnostic value as a biomarker of white matter integrity.<\/p>\n![\"Fig.](\"https:\/\/my.vanderbilt.edu\/masi\/wp-content\/uploads\/sites\/2304\n2661\/2019\/12\/1-s2.0-S0730725X1830540X-gr1-300x275.jpg\")
Fig. 1. Effects of incorrect b-vector tables. Diffusion encoded color (DEC) maps and vector maps are shown for a correct gradient table (A), a table with a flipped component (B), and one with permuted components (C). With a flipped component, the DEC map may appear correct, but unit vectors will be flipped in one plane (coronal plane in this example), and will appear correct in another (axial). For a permuted table, both the color maps and vectors will be erroneous. In both (B) and (C), directions are incorrect, and subsequent analysis and tractography will also be erroneous. DEC and vector maps are colored red, green, and blue for diffusion primarily in the right\/left, anterior\/posterior, and superior\/inferior directions, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)<\/figcaption><\/figure>\n<\/div>\n","protected":false},"excerpt":{"rendered":"Kurt G Schilling, Fang-Cheng Yeh, Vishwesh Nath, Colin Hansen, Owen Williams, Susan Resnick, Adam W. Anderson, Bennett A. Landman. \u201cA fiber coherence\u00a0index for quality control of B-table orientation in diffusion MRI scans\u201d. Magnetic Resonance Imaging. 2019.\u00a0doi: 10.1016\/j.mri.2019.01.018 Full text:\u00a0https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=fiber+coherence+b-table Abstract Purpose The diffusion MRI \u201cb-vector\u201d table describing the diffusion sensitization direction can be flipped and…<\/p>\n","protected":false},"author":7364,"featured_media":2308,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[33,9,132,1],"tags":[],"class_list":["post-2307","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-diffusion-tensor-imaging","category-diffusion-weighted-mri","category-harmonization","category-news"],"_links":{"self":[{"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/2307","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/users\/7364"}],"replies":[{"embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/comments?post=2307"}],"version-history":[{"count":2,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/2307\/revisions"}],"predecessor-version":[{"id":2310,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/2307\/revisions\/2310"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/media\/2308"}],"wp:attachment":[{"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/media?parent=2307"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/categories?post=2307"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/tags?post=2307"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Purpose<\/h3>\n
The diffusion MRI<\/a> \u201cb-vector\u201d table describing the diffusion<\/a> sensitization direction can be flipped and permuted in dimension due to different orientation conventions used in scanners and incorrect or improperly utilized file formats. This can lead to incorrect fiber orientation estimates and subsequent tractography<\/a> failure. Here, we present an automated quality control procedure to detect when the b-table is flipped and\/or permuted incorrectly.<\/p>\n<\/div>\n We define a \u201cfiber coherence index\u201d to describe how well fibers are connected to each other, and use it to automatically detect the correct configuration of b-vectors. We examined the performance on 3981 research subject scans (Baltimore Longitudinal Study of Aging), 1065 normal subject scans of high image quality (Human Connectome<\/a> Project), and 202 patient scans (Vanderbilt University Medical Center), as well as 9 in-vivo and 9 ex-vivo<\/a> animal data.<\/p>\n<\/div>\n The coherence index resulted in a 99.9% (3979\/3981) and 100% (1065\/1065) success rate in normal subject scans, 98% (198\/202) in patient scans, and 100% (18\/18) in both in-vivo and ex-vivo animal data in detecting the correct gradient table in datasets without severe image artifacts. The four failing cases (4\/202) in patient scans, and two failures in healthy subject scans (2\/3981), all showed prominent motion or signal dropout artifacts.<\/p>\n<\/div>\n The fiber coherence measure can be used as an automatic quality assurance check in any diffusion analysis pipeline. Additionally, the success of this fiber coherence measure suggests potential broader applications, including evaluating data quality, or even providing diagnostic value as a biomarker of white matter integrity.<\/p>\n Kurt G Schilling, Fang-Cheng Yeh, Vishwesh Nath, Colin Hansen, Owen Williams, Susan Resnick, Adam W. Anderson, Bennett A. Landman. \u201cA fiber coherence\u00a0index for quality control of B-table orientation in diffusion MRI scans\u201d. Magnetic Resonance Imaging. 2019.\u00a0doi: 10.1016\/j.mri.2019.01.018 Full text:\u00a0https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/?term=fiber+coherence+b-table Abstract Purpose The diffusion MRI \u201cb-vector\u201d table describing the diffusion sensitization direction can be flipped and…<\/p>\n","protected":false},"author":7364,"featured_media":2308,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[33,9,132,1],"tags":[],"class_list":["post-2307","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-diffusion-tensor-imaging","category-diffusion-weighted-mri","category-harmonization","category-news"],"_links":{"self":[{"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/2307","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/users\/7364"}],"replies":[{"embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/comments?post=2307"}],"version-history":[{"count":2,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/2307\/revisions"}],"predecessor-version":[{"id":2310,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/2307\/revisions\/2310"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/media\/2308"}],"wp:attachment":[{"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/media?parent=2307"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/categories?post=2307"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/tags?post=2307"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Methods<\/h3>\n
Results<\/h3>\n
Conclusions<\/h3>\n