http://www.nitrc.org/forum/forum.php?forum_id=8077 <table border="0" width="100%"><tr><td align="left"/></tr></table> <p><b>Improving Functional MRI Registration Using Whole-Brain Functional Correlation Tensors.</b></p> <p>Med Image Comput Comput Assist Interv. 2017 Sep;10433:416-423</p> <p>Authors: Zhou Y, Yap PT, Zhang H, Zhang L, Feng Q, Shen D</p> <p>Abstract<br/> Population studies of brain function with resting-state functional magnetic resonance imaging (rs-fMRI) largely rely on the accurate inter-subject registration of functional areas. This is typically achieved through registration of the corresponding T1-weighted MR images with more structural details. However, accumulating evidence has suggested that such strategy cannot well-align functional regions which are not necessarily confined by the anatomical boundaries defined by the T1-weighted MR images. To mitigate this problem, various registration algorithms based directly on rs-fMRI data have been developed, most of which have utilized functional connectivity (FC) as features for registration. However, most of the FC-based registration methods usually extract the functional features only from the thin and highly curved cortical grey matter (GM), posing a great challenge in accurately estimating the whole-brain deformation field. In this paper, we demonstrate that the additional useful functional features can be extracted from brain regions beyond the GM, particularly, white-matter (WM) based on rs-fMRI, for improving the overall functional registration. Specifically, we quantify the local anisotropic correlation patterns of the blood oxygenation level-dependent (BOLD) signals, modeled by functional correlation tensors (FCTs), in both GM and WM. Functional registration is then performed based on multiple components of the whole-brain FCTs using a multichannel Large Deformation Diffeomorphic Metric Mapping (mLDDMM) algorithm. Experimental results show that our proposed method achieves superior functional registration performance, compared with other conventional registration methods.<br/> </p><p>PMID: 29226283 [PubMed - in process]</p> en-us Copyright 2000-2026 NITRC OSI Sun, 03 May 2026 0:19:32 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator