Multifold Acceleration of Diffusion MRI via Deep Learning Reconstruction from Slice-Undersampled Data

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

doi:10.1007/978-3-030-20351-1_41

Multifold Acceleration of Diffusion MRI via Deep Learning Reconstruction from Slice-Undersampled Data

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

^*Corresponding author for this work

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

18 Citations (Scopus)

Abstract

Diffusion MRI (dMRI), while powerful for characterization of tissue microstructure, suffers from long acquisition time. In this paper, we present a method for effective diffusion MRI reconstruction from slice-undersampled data. Instead of 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 wavevectors can be harnessed using graph convolutional neural networks for reconstruction of the full DW volumes. The experimental results indicate a high acceleration factor of upÂ to 5 can be achieved with minimal information loss.

Original language	English
Title of host publication	Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings
Editors	Albert C.S. Chung, James C. Gee, Paul A. Yushkevich, Siqi Bao
Publisher	Springer Verlag
Pages	530-541
Number of pages	12
ISBN (Print)	9783030203504
DOIs	https://doi.org/10.1007/978-3-030-20351-1_41
Publication status	Published - 2019
Event	26th International Conference on Information Processing in Medical Imaging, IPMI 2019 - Hong Kong, China Duration: 2019 Jun 2 → 2019 Jun 7

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11492 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	26th International Conference on Information Processing in Medical Imaging, IPMI 2019
Country/Territory	China
City	Hong Kong
Period	19/6/2 → 19/6/7

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

10.1007/978-3-030-20351-1_41

Cite this

Hong, Y., Chen, G., Yap, P. T., & Shen, D. (2019). Multifold Acceleration of Diffusion MRI via Deep Learning Reconstruction from Slice-Undersampled Data. In A. C. S. Chung, J. C. Gee, P. A. Yushkevich, & S. Bao (Eds.), Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings (pp. 530-541). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11492 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-20351-1_41

Multifold Acceleration of Diffusion MRI via Deep Learning Reconstruction from Slice-Undersampled Data. / Hong, Yoonmi; Chen, Geng; Yap, Pew Thian et al.
Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings. ed. / Albert C.S. Chung; James C. Gee; Paul A. Yushkevich; Siqi Bao. Springer Verlag, 2019. p. 530-541 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11492 LNCS).

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

Hong, Y, Chen, G, Yap, PT & Shen, D 2019, Multifold Acceleration of Diffusion MRI via Deep Learning Reconstruction from Slice-Undersampled Data. in ACS Chung, JC Gee, PA Yushkevich & S Bao (eds), Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11492 LNCS, Springer Verlag, pp. 530-541, 26th International Conference on Information Processing in Medical Imaging, IPMI 2019, Hong Kong, China, 19/6/2. https://doi.org/10.1007/978-3-030-20351-1_41

Hong Y, Chen G, Yap PT, Shen D. Multifold Acceleration of Diffusion MRI via Deep Learning Reconstruction from Slice-Undersampled Data. In Chung ACS, Gee JC, Yushkevich PA, Bao S, editors, Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings. Springer Verlag. 2019. p. 530-541. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-20351-1_41

Hong, Yoonmi ; Chen, Geng ; Yap, Pew Thian et al. / Multifold Acceleration of Diffusion MRI via Deep Learning Reconstruction from Slice-Undersampled Data. Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings. editor / Albert C.S. Chung ; James C. Gee ; Paul A. Yushkevich ; Siqi Bao. Springer Verlag, 2019. pp. 530-541 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{da6568712e9848b6ad0ff0170f2fec1b,

title = "Multifold Acceleration of Diffusion MRI via Deep Learning Reconstruction from Slice-Undersampled Data",

abstract = "Diffusion MRI (dMRI), while powerful for characterization of tissue microstructure, suffers from long acquisition time. In this paper, we present a method for effective diffusion MRI reconstruction from slice-undersampled data. Instead of 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 wavevectors can be harnessed using graph convolutional neural networks for reconstruction of the full DW volumes. The experimental results indicate a high acceleration factor of up{\^A} to 5 can be achieved with minimal information loss.",

keywords = "Accelerated acquisition, Adversarial learning, Diffusion MRI, Graph CNN, Super resolution",

author = "Yoonmi Hong and Geng Chen and Yap, \{Pew Thian\} and Dinggang Shen",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 26th International Conference on Information Processing in Medical Imaging, IPMI 2019 ; Conference date: 02-06-2019 Through 07-06-2019",

year = "2019",

doi = "10.1007/978-3-030-20351-1\_41",

language = "English",

isbn = "9783030203504",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "530--541",

editor = "Chung, \{Albert C.S.\} and Gee, \{James C.\} and Yushkevich, \{Paul A.\} and Siqi Bao",

booktitle = "Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings",

}

TY - GEN

T1 - Multifold Acceleration of Diffusion MRI via Deep Learning Reconstruction from Slice-Undersampled Data

AU - Hong, Yoonmi

AU - Chen, Geng

AU - Yap, Pew Thian

AU - Shen, Dinggang

PY - 2019

Y1 - 2019

N2 - Diffusion MRI (dMRI), while powerful for characterization of tissue microstructure, suffers from long acquisition time. In this paper, we present a method for effective diffusion MRI reconstruction from slice-undersampled data. Instead of 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 wavevectors can be harnessed using graph convolutional neural networks for reconstruction of the full DW volumes. The experimental results indicate a high acceleration factor of upÂ to 5 can be achieved with minimal information loss.

AB - Diffusion MRI (dMRI), while powerful for characterization of tissue microstructure, suffers from long acquisition time. In this paper, we present a method for effective diffusion MRI reconstruction from slice-undersampled data. Instead of 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 wavevectors can be harnessed using graph convolutional neural networks for reconstruction of the full DW volumes. The experimental results indicate a high acceleration factor of upÂ to 5 can be achieved with minimal information loss.

KW - Accelerated acquisition

KW - Adversarial learning

KW - Diffusion MRI

KW - Graph CNN

KW - Super resolution

UR - https://www.scopus.com/pages/publications/85066131537

U2 - 10.1007/978-3-030-20351-1_41

DO - 10.1007/978-3-030-20351-1_41

M3 - Conference contribution

AN - SCOPUS:85066131537

SN - 9783030203504

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 530

EP - 541

BT - Information Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings

A2 - Chung, Albert C.S.

A2 - Gee, James C.

A2 - Yushkevich, Paul A.

A2 - Bao, Siqi

PB - Springer Verlag

T2 - 26th International Conference on Information Processing in Medical Imaging, IPMI 2019

Y2 - 2 June 2019 through 7 June 2019

ER -

Multifold Acceleration of Diffusion MRI via Deep Learning Reconstruction from Slice-Undersampled Data

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this