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

doi:10.1007/978-3-030-32248-9_59

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
Editors	Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	529-537
Number of pages	9
ISBN (Print)	9783030322472
DOIs	https://doi.org/10.1007/978-3-030-32248-9_59
Publication status	Published - 2019
Event	22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China Duration: 2019 Oct 13 → 2019 Oct 17

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11766 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Country/Territory	China
City	Shenzhen
Period	19/10/13 → 19/10/17

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

Keywords

Accelerated acquisition
Adversarial learning
Diffusion MRI
Graph CNN
Super resolution

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-32248-9_59

Cite this

Hong, Y., Chen, G., Yap, P. T., & Shen, D. (2019). Reconstructing High-Quality Diffusion MRI Data from Orthogonal Slice-Undersampled Data Using Graph Convolutional Neural Networks. In D. Shen, P.-T. Yap, T. Liu, T. M. Peters, A. Khan, L. H. Staib, C. Essert, & S. Zhou (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings (pp. 529-537). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11766 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-32248-9_59

Reconstructing High-Quality Diffusion MRI Data from Orthogonal Slice-Undersampled Data Using Graph Convolutional Neural Networks. / Hong, Yoonmi; Chen, Geng; Yap, Pew Thian et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. ed. / Dinggang Shen; Pew-Thian Yap; Tianming Liu; Terry M. Peters; Ali Khan; Lawrence H. Staib; Caroline Essert; Sean Zhou. Springer Science and Business Media Deutschland GmbH, 2019. p. 529-537 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11766 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Hong, Y, Chen, G, Yap, PT & Shen, D 2019, Reconstructing High-Quality Diffusion MRI Data from Orthogonal Slice-Undersampled Data Using Graph Convolutional Neural Networks. in D Shen, P-T Yap, T Liu, TM Peters, A Khan, LH Staib, C Essert & S Zhou (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11766 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 529-537, 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, China, 19/10/13. https://doi.org/10.1007/978-3-030-32248-9_59

Hong Y, Chen G, Yap PT, Shen D. Reconstructing High-Quality Diffusion MRI Data from Orthogonal Slice-Undersampled Data Using Graph Convolutional Neural Networks. In Shen D, Yap PT, Liu T, Peters TM, Khan A, Staib LH, Essert C, Zhou S, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2019. p. 529-537. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-32248-9_59

Hong, Yoonmi ; Chen, Geng ; Yap, Pew Thian et al. / Reconstructing High-Quality Diffusion MRI Data from Orthogonal Slice-Undersampled Data Using Graph Convolutional Neural Networks. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. editor / Dinggang Shen ; Pew-Thian Yap ; Tianming Liu ; Terry M. Peters ; Ali Khan ; Lawrence H. Staib ; Caroline Essert ; Sean Zhou. Springer Science and Business Media Deutschland GmbH, 2019. pp. 529-537 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "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.",

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

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