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Abstract #3511

Model-Based Reconstruction for Undersampled High Resolution Diffusion Tensor Imaging Combined with Simultaneous Multi-Slice Acquisitions

Zijing Dong1, Erpeng Dai1, Fuyixue Wang1,2, Yuantao Gu3, Chun Yuan1,4, and Hua Guo1

1Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, People's Republic of China, 2Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, United States, 3Department of Electronic Engineering, Tsinghua University, Beijing, People's Republic of China, 4Vascular Imaging Laboratory, Department of Radiology, University of Washington, Seattle, WA, United States

Multi-shot interleaved EPI is an effective method to acquire high-resolution and less distorted diffusion weighted images, but with relatively low acquisition efficiency, especially for diffusion tensor imaging. Here, a novel model-based reconstruction is proposed for accelerated multi-shot diffusion imaging with simultaneous multi-slice (SMS) and partially parallel imaging (PPI). The method can directly estimate diffusion tensors from the undersampled k-space data, by integrating information of all shots and diffusion encoding directions. Simulation and in-vivo experiment demonstrated that the proposed method can achieve higher acceleration efficiency and improved accuracy in tensor estimation compared with conventional 2D GRAPPA.

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