Merry P. Mani1, Tong Zhu2,
Jianhui Zhong2, Mathews Jacob3
1Rochester Center for Brain
Imaging, Electrical & Computer Engineering, University of Rochester,
Rochester, NY, United States; 2Imaging Sciences, University of
Rochester, Rochester, NY, United States; 3Biomedical Engineering,
University of Rochester, Rochester, NY, United States
The aim of the study is to test the feasibility of using compressed sensing to accelerate the high angular sampling schemes for DTI. A realistic but simulated k-space data was randomly under-sampled in the frequency-diffusion domain with 256 diffusion directions. By making use of the sparsity of the orientation information in each voxel and choosing an appropriate set of basis functions, the diffusion-weighted images were reconstructed using compressed sensing without aliasing. Up to 8 fold acceleration could be achieved within reasonable reconstruction errors. The technique allows to fit parametric models to high angular resolution DW data due to the chosen set of basis functions.
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