Reconstructing High-Quality Diffusion MRI Data from Orthogonal Slice-Undersampled Data Using Graph Convolutional Neural Networks

Yoonmi Hong, Geng Chen, Pew Thian Yap, Dinggang Shen

科研成果: 书/报告/会议事项章节会议稿件同行评审

8 引用 (Scopus)

摘要

Diffusion MRI (dMRI), while powerful for the characterization of tissue microstructure, suffers from long acquisition times. In this paper, we propose a super-resolution (SR) reconstruction method based on orthogonal slice-undersampling for accelerated dMRI acquisition. Instead of scanning full diffusion-weighted (DW) image volumes, only a subsample of equally-spaced slices need to be acquired. We show that complementary information from DW volumes corresponding to different diffusion wave-vectors can be harnessed using graph convolutional neural networks for reconstruction of the full DW volumes. We demonstrate that our SR reconstruction method outperforms typical interpolation methods and mitigates partial volume effects. Experimental results indicate that acceleration up to a factor of 5 can be achieved with minimal information loss.

源语言英语
主期刊名Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
编辑Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
出版商Springer Science and Business Media Deutschland GmbH
529-537
页数9
ISBN(印刷版)9783030322472
DOI
出版状态已出版 - 2019
已对外发布
活动22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, 中国
期限: 13 10月 201917 10月 2019

出版系列

姓名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11766 LNCS
ISSN(印刷版)0302-9743
ISSN(电子版)1611-3349

会议

会议22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
国家/地区中国
Shenzhen
时期13/10/1917/10/19

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