Meeting Banner
Abstract #3518

Accelerating Diffusion Tensor Imaging Using a Parametric Manifold Model

Chaoyi Zhang1, Dan Wu2, Jiangyang Zhang3, Dong Liang4, Jingyuan Lyu1, Ukash Nakarmi1, Rong-Rong Chen5, and Leslie Ying1,6

1Electrical Engineering, University at Buffalo,State University of New York, Buffalo, NY, United States, 2Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States, 3Radiology, New York University School of Medicine, New York City, NY, United States, 4Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Shenzhen, People's Republic of China, 5Electrical & Computer Engineering, University of Utah, Salt Lake City, UT, United States, 6Biomedical Engineering, University at Buffalo, State University at New York, Buffalo, NY, United States

This abstract presents a novel method for diffusion tensor image (DTI) directly from highly under-sampled data acquired at multiple diffusion gradients. This method formulates the diffusion tensor estimation as a problem of parametric manifold recovery. We solve the recovery problem by alternatively shrinking the diffusion weighted images, estimating diffusion tensor, and enforcing data consistency constraint. The experimental results demonstrate that the proposed method is able to reconstruct the diffusion tensors accurately at high acceleration factors with low computational complexity.

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

Join Here