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Abstract #4717

A Novel Variable-Density Sampling Strategy for 3D Diffusion-Tensor Imaging with 3D-MUSER and Compressed Sensing

Xiaorui Xu1, Liyuan Liang1, Xiaoxi Liu2, and Hing-Chiu Chang3
1Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, Hong Kong, 2Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States, 3Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong


3D multi-slab multi-shot diffusion-weighted EPI can enable high-resolution diffusion-tensor imaging (DTI). Furthermore, in order to avoid the slab boundary artefacts, 3D-MUSER was proposed to enable 3D phase correction for effectively reconstructing nearly whole brain 3D DTI data acquired with only a single slab. Compressed sensing (CS) was also combined with 3D-MUSER to further reduce the scan time, but the previously proposed pseudo-random sampling strategy was still suboptimal for CS reconstruction. Therefore, we propose a novel variable-density k-space sampling strategy that is compatible with 3D-MUSER and able to achieve highly-accelerated and high-quality 3D DTI by taking the full advantage of CS.

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