VS-Net: variable splitting network for accelerated parallel MRI reconstruction

Jinming Duan; Jo Schlemper; Chen Qin; Cheng Ouyang; Wenjia Bai; Carlo Biffi; Ghalib Bello; Ben Statton; Declan P. O’regan; Daniel Rueckert

doi:10.1007/978-3-030-32251-9_78

VS-Net: variable splitting network for accelerated parallel MRI reconstruction

Jinming Duan, Jo Schlemper, Chen Qin, Cheng Ouyang, Wenjia Bai, Carlo Biffi, Ghalib Bello, Ben Statton, Declan P. O’regan, Daniel Rueckert

Computer Science

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

13 Citations (Scopus)

Abstract

In this work, we propose a deep learning approach for parallel magnetic resonance imaging (MRI) reconstruction, termed a variable splitting network (VS-Net), for an efficient, high-quality reconstruction of undersampled multi-coil MR data. We formulate the generalized parallel compressed sensing reconstruction as an energy minimization problem, for which a variable splitting optimization method is derived. Based on this formulation we propose a novel, end-to-end trainable deep neural network architecture by unrolling the resulting iterative process of such variable splitting scheme. VS-Net is evaluated on complex valued multi-coil knee images for 4-fold and 6-fold acceleration factors. We show that VS-Net outperforms state-of-the-art deep learning reconstruction algorithms, in terms of reconstruction accuracy and perceptual quality. Our code is publicly available at https://github.com/j-duan/VS-Net.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
Subtitle of host publication	22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part IV
Editors	Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan
Publisher	Springer
Chapter	78
Pages	713-722
Number of pages	10
Edition	1
ISBN (Electronic)	9783030322519
ISBN (Print)	9783030322502
DOIs	https://doi.org/10.1007/978-3-030-32251-9_78
Publication status	Published - 10 Oct 2019
Event	22nd International Conference on Medical Image Computing and Computer-Assisted Intervention - Shenzhen, China Duration: 13 Oct 2019 → 17 Oct 2019

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	11767
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

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

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-030-32251-9_78Licence: None: All rights reserved

Cite this

Duan, J., Schlemper, J., Qin, C., Ouyang, C., Bai, W., Biffi, C., Bello, G., Statton, B., O’regan, D. P., & Rueckert, D. (2019). VS-Net: variable splitting network for accelerated parallel MRI reconstruction. In D. Shen, T. Liu, T. M. Peters, L. H. Staib, C. Essert, S. Zhou, P-T. Yap, & A. Khan (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part IV (1 ed., pp. 713-722). (Lecture Notes in Computer Science; Vol. 11767). Springer. https://doi.org/10.1007/978-3-030-32251-9_78

Duan, Jinming ; Schlemper, Jo ; Qin, Chen et al. / VS-Net : variable splitting network for accelerated parallel MRI reconstruction. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part IV. editor / Dinggang Shen ; Tianming Liu ; Terry M. Peters ; Lawrence H. Staib ; Caroline Essert ; Sean Zhou ; Pew-Thian Yap ; Ali Khan. 1. ed. Springer, 2019. pp. 713-722 (Lecture Notes in Computer Science).

@inproceedings{a0644deb4b7f4118ab7cbe79cd437b3d,

title = "VS-Net: variable splitting network for accelerated parallel MRI reconstruction",

abstract = "In this work, we propose a deep learning approach for parallel magnetic resonance imaging (MRI) reconstruction, termed a variable splitting network (VS-Net), for an efficient, high-quality reconstruction of undersampled multi-coil MR data. We formulate the generalized parallel compressed sensing reconstruction as an energy minimization problem, for which a variable splitting optimization method is derived. Based on this formulation we propose a novel, end-to-end trainable deep neural network architecture by unrolling the resulting iterative process of such variable splitting scheme. VS-Net is evaluated on complex valued multi-coil knee images for 4-fold and 6-fold acceleration factors. We show that VS-Net outperforms state-of-the-art deep learning reconstruction algorithms, in terms of reconstruction accuracy and perceptual quality. Our code is publicly available at https://github.com/j-duan/VS-Net.",

author = "Jinming Duan and Jo Schlemper and Chen Qin and Cheng Ouyang and Wenjia Bai and Carlo Biffi and Ghalib Bello and Ben Statton and O{\textquoteright}regan, {Declan P.} and Daniel Rueckert",

year = "2019",

month = oct,

day = "10",

doi = "10.1007/978-3-030-32251-9_78",

language = "English",

isbn = "9783030322502",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "713--722",

editor = "Shen, {Dinggang } and Liu, {Tianming } and Peters, {Terry M. } and Staib, {Lawrence H. } and Essert, {Caroline } and Zhou, {Sean } and Yap, {Pew-Thian } and Khan, {Ali }",

booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2019",

edition = "1",

note = "22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 ; Conference date: 13-10-2019 Through 17-10-2019",

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Duan, J, Schlemper, J, Qin, C, Ouyang, C, Bai, W, Biffi, C, Bello, G, Statton, B, O’regan, DP & Rueckert, D 2019, VS-Net: variable splitting network for accelerated parallel MRI reconstruction. in D Shen, T Liu, TM Peters, LH Staib, C Essert, S Zhou, P-T Yap & A Khan (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part IV. 1 edn, Lecture Notes in Computer Science, vol. 11767, Springer, pp. 713-722, 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, Shenzhen, China, 13/10/19. https://doi.org/10.1007/978-3-030-32251-9_78

VS-Net: variable splitting network for accelerated parallel MRI reconstruction. / Duan, Jinming; Schlemper, Jo; Qin, Chen et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part IV. ed. / Dinggang Shen; Tianming Liu; Terry M. Peters; Lawrence H. Staib; Caroline Essert; Sean Zhou; Pew-Thian Yap; Ali Khan. 1. ed. Springer, 2019. p. 713-722 (Lecture Notes in Computer Science; Vol. 11767).

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

TY - GEN

T1 - VS-Net

T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention

AU - Duan, Jinming

AU - Schlemper, Jo

AU - Qin, Chen

AU - Ouyang, Cheng

AU - Bai, Wenjia

AU - Biffi, Carlo

AU - Bello, Ghalib

AU - Statton, Ben

AU - O’regan, Declan P.

AU - Rueckert, Daniel

PY - 2019/10/10

Y1 - 2019/10/10

N2 - In this work, we propose a deep learning approach for parallel magnetic resonance imaging (MRI) reconstruction, termed a variable splitting network (VS-Net), for an efficient, high-quality reconstruction of undersampled multi-coil MR data. We formulate the generalized parallel compressed sensing reconstruction as an energy minimization problem, for which a variable splitting optimization method is derived. Based on this formulation we propose a novel, end-to-end trainable deep neural network architecture by unrolling the resulting iterative process of such variable splitting scheme. VS-Net is evaluated on complex valued multi-coil knee images for 4-fold and 6-fold acceleration factors. We show that VS-Net outperforms state-of-the-art deep learning reconstruction algorithms, in terms of reconstruction accuracy and perceptual quality. Our code is publicly available at https://github.com/j-duan/VS-Net.

AB - In this work, we propose a deep learning approach for parallel magnetic resonance imaging (MRI) reconstruction, termed a variable splitting network (VS-Net), for an efficient, high-quality reconstruction of undersampled multi-coil MR data. We formulate the generalized parallel compressed sensing reconstruction as an energy minimization problem, for which a variable splitting optimization method is derived. Based on this formulation we propose a novel, end-to-end trainable deep neural network architecture by unrolling the resulting iterative process of such variable splitting scheme. VS-Net is evaluated on complex valued multi-coil knee images for 4-fold and 6-fold acceleration factors. We show that VS-Net outperforms state-of-the-art deep learning reconstruction algorithms, in terms of reconstruction accuracy and perceptual quality. Our code is publicly available at https://github.com/j-duan/VS-Net.

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U2 - 10.1007/978-3-030-32251-9_78

DO - 10.1007/978-3-030-32251-9_78

M3 - Conference contribution

SN - 9783030322502

T3 - Lecture Notes in Computer Science

SP - 713

EP - 722

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

A2 - Shen, Dinggang

A2 - Liu, Tianming

A2 - Peters, Terry M.

A2 - Staib, Lawrence H.

A2 - Essert, Caroline

A2 - Zhou, Sean

A2 - Yap, Pew-Thian

A2 - Khan, Ali

PB - Springer

Y2 - 13 October 2019 through 17 October 2019

ER -

Duan J, Schlemper J, Qin C, Ouyang C, Bai W, Biffi C et al. VS-Net: variable splitting network for accelerated parallel MRI reconstruction. In Shen D, Liu T, Peters TM, Staib LH, Essert C, Zhou S, Yap P-T, Khan A, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part IV. 1 ed. Springer. 2019. p. 713-722. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-32251-9_78

VS-Net: variable splitting network for accelerated parallel MRI reconstruction

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