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Abstract #1016

Improved SLIPEN (iSLIPEN) for Three-Dimensional Multi-slab Diffusion-Weighted Imaging by Partial Fourier and Prior Information

Xiaorui Xu1, Shihui Chen2, Liyuan Liang2,3, Chenglang Yuan2, Hailin Xiong2, and Hing-Chiu Chang2,3
1Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, Hong Kong, 2Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong, 3Multi-Scale Medical Robotics Center, Hong Kong, Hong Kong

Synopsis

Keywords: DWI/DTI/DKI, Diffusion Tensor Imaging

Motivation: SLIPEN is a promising technique to obtain 3D multi-slab DWI without suffering slab boundary artifacts. However, its performance is degraded when encountering limited signal SNR.

Goal(s): An improved SLIPEN is desired to achieve robust perfomance regardless of limited signal SNR.

Approach: Partial Fourier was applied to design an optimized sampling pattern and prior information was also incorporated into the model to improve the performance.

Results: The improved SLIPEN could achieve comparable results to gold standard for in-vivo DWI images and DTI maps, with the need of only one third of gold standard data.

Impact: 3D isotropic high-resolution DWI without suffering from slab boundary artifacts can be robustly achieved by our method with the use of 2D navigator, therefore benefiting the neuroscience study in evaluating crossing and kissing fibers.

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