Abstract:<\/h2>\n
Diffusion<\/span> tensor<\/span> imaging<\/span> enables in vivo investigation of tissue cytoarchitecture through parameter contrasts sensitive to water diffusion<\/span> barriers at the micrometer level. Parameters<\/span> are derived through an estimation process that is susceptible to noise and artifacts. Estimated parameters<\/span> (e.g., fractional anisotropy) exhibit both variability and bias<\/span> relative to the true parameter value estimated from a hypothetical noise-free acquisition. Herein, we present the use of the simulation and extrapolation (SIMEX<\/span>) approach for post hoc assessment<\/span> of bias<\/span> in a massively univariate imaging<\/span> setting and evaluate the potential of a SIMEX<\/span>-based bias<\/span> correction. Using simulated data with known truth models, spatially varying fractional anisotropy bias<\/span> error maps are evaluated on two independent and highly differentiated case studies. The stability of SIMEX<\/span> and its distributional properties are further evaluated on 42 empirical diffusion<\/span> tensor<\/span> imaging<\/span> datasets. Using gradient subsampling, an empirical experiment with a known true outcome is designed and SIMEX<\/span> performance is compared to the original estimator. With this approach, we find SIMEX<\/span> bias<\/span> estimates to be highly accurate offering significant reductions in parameter bias<\/span> for individual datasets and greater accuracy in averaged population-based estimates. Carolyn B. Lauzon, Ciprian Crainiceanu, Brian C. Caffo, Bennett A. Landman, \u201cAssessment of Bias in MRI Diffusion Tensor Imaging Parameters Using SIMEX\u201d, Magnetic Resonance in Medicine. 2013 Mar 1;69(3):891-902. PMC22611000 \u2020 Full Text: https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/22611000 Abstract: Diffusion tensor imaging enables in vivo investigation of tissue cytoarchitecture through parameter contrasts sensitive to water diffusion barriers at the…<\/p>\n","protected":false},"author":6299,"featured_media":280,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[33,4],"tags":[34,11,35],"class_list":["post-266","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-diffusion-tensor-imaging","category-neuroimaging","tag-diffusion-tensor-imaging","tag-dti","tag-neuroimaging"],"_links":{"self":[{"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/266","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\/6299"}],"replies":[{"embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/comments?post=266"}],"version-history":[{"count":1,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/266\/revisions"}],"predecessor-version":[{"id":301,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/266\/revisions\/301"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/media\/280"}],"wp:attachment":[{"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/media?parent=266"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/categories?post=266"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/tags?post=266"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"Print\"](\"https:\/\/my.vanderbilt.edu\/masi\/wp-content\/uploads\/sites\/2304\n2661\/2016\/10\/nihms531393f4-1.jpg\")
<\/p>\n","protected":false},"excerpt":{"rendered":"