Meeting Banner
Abstract #1641

Predicting Diffusion Tensors from Resting-State Functional MRI

Lichi Zhang1, Han Zhang1, Qian Wang2, Pew-Thian Yap1, Xiaobo Chen1, and Dinggang Shen1

1Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, 2Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China

It has been recently reported that the spatio-temporal correlation of white matter BOLD signals in resting-state functional MRI (rs-fMRI) can be captured using functional correlation tensors (FCTs). FCTs exhibit anisotropy information similar to diffusion tensor imaging (DTI). In this work, we employ a patch-based strategy to improve the noise-robustness of FCTs. Then, we adopt regression forest to learn a mapping from FCTs to DTs. Testing using unseen images, the predicted DTs show high similarity with the actual DTs. This validates the fact that FCTs carries information that is highly correlated with DTs.

This abstract and the presentation materials are available to members only; a login is required.

Join Here