Compressive MR Fingerprinting Reconstruction with Neural Proximal Gradient Iterations

Dongdong Chen; Mike E. Davies; Mohammad Golbabaee

doi:10.1007/978-3-030-59713-9_2

Compressive MR Fingerprinting Reconstruction with Neural Proximal Gradient Iterations

Dongdong Chen, Mike E. Davies, Mohammad Golbabaee^*

^*Corresponding author for this work

School of Engineering

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

Abstract / Description of output

Consistency of the predictions with respect to the physical forward model is pivotal for reliably solving inverse problems. This consistency is mostly un-controlled in the current end-to-end deep learning methodologies proposed for the Magnetic Resonance Fingerprinting (MRF) problem. To address this, we propose PGD-Net, a learned proximal gradient descent framework that directly incorporates the forward acquisition and Bloch dynamic models within a recurrent learning mechanism. The PGD-Net adopts a compact neural proximal model for de-aliasing and quantitative inference, that can be flexibly trained on scarce MRF training datasets. Our numerical experiments show that the PGD-Net can achieve a superior quantitative inference accuracy, much smaller storage requirement, and a comparable runtime to the recent deep learning MRF baselines, while being much faster than the dictionary matching schemes. Code has been released at https://github.com/edongdongchen/PGD-Net.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
Editors	Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
Publisher	Springer
Pages	13-22
Number of pages	10
ISBN (Print)	9783030597122
DOIs	https://doi.org/10.1007/978-3-030-59713-9_2
Publication status	Published - 29 Sept 2020
Event	23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru Duration: 4 Oct 2020 → 8 Oct 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12262 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Country/Territory	Peru
City	Lima
Period	4/10/20 → 8/10/20

Keywords / Materials (for Non-textual outputs)

Compressed Sensing
Deep learning
Learned proximal gradient descent
Magnetic resonance fingerprinting

Access to Document

10.1007/978-3-030-59713-9_2

Cite this

Chen, D., Davies, M. E., & Golbabaee, M. (2020). Compressive MR Fingerprinting Reconstruction with Neural Proximal Gradient Iterations. In A. L. Martel, P. Abolmaesumi, D. Stoyanov, D. Mateus, M. A. Zuluaga, S. K. Zhou, D. Racoceanu, & L. Joskowicz (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings (pp. 13-22). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12262 LNCS). Springer. https://doi.org/10.1007/978-3-030-59713-9_2

Chen, Dongdong ; Davies, Mike E. ; Golbabaee, Mohammad. / Compressive MR Fingerprinting Reconstruction with Neural Proximal Gradient Iterations. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. editor / Anne L. Martel ; Purang Abolmaesumi ; Danail Stoyanov ; Diana Mateus ; Maria A. Zuluaga ; S. Kevin Zhou ; Daniel Racoceanu ; Leo Joskowicz. Springer, 2020. pp. 13-22 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{3e71de6b1dd9458c8e041163b6fe7016,

title = "Compressive MR Fingerprinting Reconstruction with Neural Proximal Gradient Iterations",

abstract = "Consistency of the predictions with respect to the physical forward model is pivotal for reliably solving inverse problems. This consistency is mostly un-controlled in the current end-to-end deep learning methodologies proposed for the Magnetic Resonance Fingerprinting (MRF) problem. To address this, we propose PGD-Net, a learned proximal gradient descent framework that directly incorporates the forward acquisition and Bloch dynamic models within a recurrent learning mechanism. The PGD-Net adopts a compact neural proximal model for de-aliasing and quantitative inference, that can be flexibly trained on scarce MRF training datasets. Our numerical experiments show that the PGD-Net can achieve a superior quantitative inference accuracy, much smaller storage requirement, and a comparable runtime to the recent deep learning MRF baselines, while being much faster than the dictionary matching schemes. Code has been released at https://github.com/edongdongchen/PGD-Net.",

keywords = "Compressed Sensing, Deep learning, Learned proximal gradient descent, Magnetic resonance fingerprinting",

author = "Dongdong Chen and Davies, {Mike E.} and Mohammad Golbabaee",

note = "Funding Information: Acknowledgements. We thank Pedro G{\'o}mez, Carolin Prikl and Marion Menzel from GE Healthcare for the quantitative anatomical maps dataset. DC and MD are supported by the ERC C-SENSE project (ERCADG-2015-694888). Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 ; Conference date: 04-10-2020 Through 08-10-2020",

year = "2020",

month = sep,

day = "29",

doi = "10.1007/978-3-030-59713-9_2",

language = "English",

isbn = "9783030597122",

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

publisher = "Springer",

pages = "13--22",

editor = "Martel, {Anne L.} and Purang Abolmaesumi and Danail Stoyanov and Diana Mateus and Zuluaga, {Maria A.} and Zhou, {S. Kevin} and Daniel Racoceanu and Leo Joskowicz",

booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings",

address = "United Kingdom",

}

Chen, D, Davies, ME & Golbabaee, M 2020, Compressive MR Fingerprinting Reconstruction with Neural Proximal Gradient Iterations. in AL Martel, P Abolmaesumi, D Stoyanov, D Mateus, MA Zuluaga, SK Zhou, D Racoceanu & L Joskowicz (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12262 LNCS, Springer, pp. 13-22, 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, Lima, Peru, 4/10/20. https://doi.org/10.1007/978-3-030-59713-9_2

Compressive MR Fingerprinting Reconstruction with Neural Proximal Gradient Iterations. / Chen, Dongdong; Davies, Mike E.; Golbabaee, Mohammad.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. ed. / Anne L. Martel; Purang Abolmaesumi; Danail Stoyanov; Diana Mateus; Maria A. Zuluaga; S. Kevin Zhou; Daniel Racoceanu; Leo Joskowicz. Springer, 2020. p. 13-22 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12262 LNCS).

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

TY - GEN

T1 - Compressive MR Fingerprinting Reconstruction with Neural Proximal Gradient Iterations

AU - Chen, Dongdong

AU - Davies, Mike E.

AU - Golbabaee, Mohammad

N1 - Funding Information: Acknowledgements. We thank Pedro Gómez, Carolin Prikl and Marion Menzel from GE Healthcare for the quantitative anatomical maps dataset. DC and MD are supported by the ERC C-SENSE project (ERCADG-2015-694888). Publisher Copyright: © 2020, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/9/29

Y1 - 2020/9/29

N2 - Consistency of the predictions with respect to the physical forward model is pivotal for reliably solving inverse problems. This consistency is mostly un-controlled in the current end-to-end deep learning methodologies proposed for the Magnetic Resonance Fingerprinting (MRF) problem. To address this, we propose PGD-Net, a learned proximal gradient descent framework that directly incorporates the forward acquisition and Bloch dynamic models within a recurrent learning mechanism. The PGD-Net adopts a compact neural proximal model for de-aliasing and quantitative inference, that can be flexibly trained on scarce MRF training datasets. Our numerical experiments show that the PGD-Net can achieve a superior quantitative inference accuracy, much smaller storage requirement, and a comparable runtime to the recent deep learning MRF baselines, while being much faster than the dictionary matching schemes. Code has been released at https://github.com/edongdongchen/PGD-Net.

AB - Consistency of the predictions with respect to the physical forward model is pivotal for reliably solving inverse problems. This consistency is mostly un-controlled in the current end-to-end deep learning methodologies proposed for the Magnetic Resonance Fingerprinting (MRF) problem. To address this, we propose PGD-Net, a learned proximal gradient descent framework that directly incorporates the forward acquisition and Bloch dynamic models within a recurrent learning mechanism. The PGD-Net adopts a compact neural proximal model for de-aliasing and quantitative inference, that can be flexibly trained on scarce MRF training datasets. Our numerical experiments show that the PGD-Net can achieve a superior quantitative inference accuracy, much smaller storage requirement, and a comparable runtime to the recent deep learning MRF baselines, while being much faster than the dictionary matching schemes. Code has been released at https://github.com/edongdongchen/PGD-Net.

KW - Compressed Sensing

KW - Deep learning

KW - Learned proximal gradient descent

KW - Magnetic resonance fingerprinting

UR - http://www.scopus.com/inward/record.url?scp=85092705868&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-59713-9_2

DO - 10.1007/978-3-030-59713-9_2

M3 - Conference contribution

AN - SCOPUS:85092705868

SN - 9783030597122

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

SP - 13

EP - 22

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings

A2 - Martel, Anne L.

A2 - Abolmaesumi, Purang

A2 - Stoyanov, Danail

A2 - Mateus, Diana

A2 - Zuluaga, Maria A.

A2 - Zhou, S. Kevin

A2 - Racoceanu, Daniel

A2 - Joskowicz, Leo

PB - Springer

T2 - 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

Y2 - 4 October 2020 through 8 October 2020

ER -

Chen D, Davies ME, Golbabaee M. Compressive MR Fingerprinting Reconstruction with Neural Proximal Gradient Iterations. In Martel AL, Abolmaesumi P, Stoyanov D, Mateus D, Zuluaga MA, Zhou SK, Racoceanu D, Joskowicz L, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. Springer. 2020. p. 13-22. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-59713-9_2

Compressive MR Fingerprinting Reconstruction with Neural Proximal Gradient Iterations

Abstract / Description of output

Publication series

Conference

Keywords / Materials (for Non-textual outputs)

Access to Document

Other files and links

Fingerprint

C-SENSE: Exploiting low dimensional models in sensing, computation and signal processing

CQ-MRI: Compressed Quantitative MRI

Cite this

Compressive MR Fingerprinting Reconstruction with Neural Proximal Gradient Iterations

Abstract / Description of output

Publication series

Conference

Keywords / Materials (for Non-textual outputs)

Access to Document

Other files and links

Fingerprint

Projects

C-SENSE: Exploiting low dimensional models in sensing, computation and signal processing

CQ-MRI: Compressed Quantitative MRI

Cite this