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 Ying^1,6

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.

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