Abstract #3343

Acceleration of 3D diffusion MRI using a kernel low rank compressed method

Chaoyi Zhang¹, Tanzil Mahmud Arefin², Ukash Nakarmi³, Hongyu Li¹, Dong Liang⁴, Jiangyang Zhang², and Leslie Ying^1,5

¹Electrical Engineering, University at Buffalo, State University of New York, buffalo, NY, United States, ²Radiology, New York University School of Medicine, New York City, NY, United States, ³Radiology & Electrical Engineering, Stanford University, Stanford, CA, United States, ⁴Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, China, ⁵Biomedical Engineering, University at Buffalo,State University of New York, buffalo, NY, United States

Diffusion MRI has showed great potential in probing tissue microstructure and brain structural connectivity. However, high-resolution diffusion MRI with multiple direction is limited by the lengthy scan time. In this abstract, we apply a kernel low rank model to accelerate diffusion imaging by undersampling the k-space. This method is validated using high-resolution mouse brain datasets. Compared with the conventional compressed sensing method, the proposed method demonstrate more accurate mean diffusivity, fractional anisotropy and fiber orientation distribution estimates with acceleration factors up to 8.

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