Joint Multi-anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions

Patricia M Johnson; Matthew J Muckley; Mary Bruno; Erich Kobler; Kerstin Hammernik; Thomas Pock; Florian Knoll

doi:10.1007/978-3-030-33843-5_7

Joint Multi-anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions

Patricia M Johnson, Matthew J Muckley, Mary Bruno, Erich Kobler, Kerstin Hammernik, Thomas Pock, Florian Knoll

Institute of Computer Graphics and Vision (7100)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Abstract

Magnetic resonance imaging is a leading image modality for many clinical applications; however, a significant drawback is the lengthy data acquisition. This motivates the development of methods for reconstruction of sparsely sampled image data. One such technique is the Variational Network (VN), a machine learning method that generalizes traditional iterative reconstruction techniques, learning the regularization term from large amounts of image data. Previously, with the VN technique, reconstruction of 4-fold accelerated knee images was shown to be highly successful. In this work we extend the VN approach to applications beyond knee imaging and evaluate the classic VN and a newly developed Unet-VN in 5 different anatomical regions. We evaluate the networks trained individually for each anatomical area as well as jointly trained with data from all anatomical areas. The VN and Unet-VN were …

Original language	English
Title of host publication	Machine Learning for Medical Image Reconstruction
Subtitle of host publication	Second International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings
Editors	F. Knoll, A. Maier, D. Rueckert, J. Ye
Place of Publication	Cham
Publisher	Springer
Pages	71-79
ISBN (Print)	978-3-030-33842-8
DOIs	https://doi.org/10.1007/978-3-030-33843-5_7
Publication status	Published - 2019
Event	2019 International Workshop on Machine Learning for Medical Image Reconstruction - Shenzen, China Duration: 17 Oct 2019 → …

Publication series

Name	Lecture Notes in Computer Science
Volume	11905

Conference

Conference	2019 International Workshop on Machine Learning for Medical Image Reconstruction
Abbreviated title	MLMIR 2019
Country/Territory	China
City	Shenzen
Period	17/10/19 → …

Access to Document

10.1007/978-3-030-33843-5_7

Cite this

Johnson, P. M., Muckley, M. J., Bruno, M., Kobler, E., Hammernik, K., Pock, T., & Knoll, F. (2019). Joint Multi-anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions. In F. Knoll, A. Maier, D. Rueckert, & J. Ye (Eds.), Machine Learning for Medical Image Reconstruction: Second International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings (pp. 71-79). (Lecture Notes in Computer Science; Vol. 11905). Springer. https://doi.org/10.1007/978-3-030-33843-5_7

Joint Multi-anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions. / Johnson, Patricia M; Muckley, Matthew J; Bruno, Mary et al.
Machine Learning for Medical Image Reconstruction: Second International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings. ed. / F. Knoll; A. Maier; D. Rueckert; J. Ye. Cham: Springer, 2019. p. 71-79 (Lecture Notes in Computer Science; Vol. 11905).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Johnson, PM, Muckley, MJ, Bruno, M, Kobler, E, Hammernik, K, Pock, T & Knoll, F 2019, Joint Multi-anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions. in F Knoll, A Maier, D Rueckert & J Ye (eds), Machine Learning for Medical Image Reconstruction: Second International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings. Lecture Notes in Computer Science, vol. 11905, Springer, Cham, pp. 71-79, 2019 International Workshop on Machine Learning for Medical Image Reconstruction, Shenzen, China, 17/10/19. https://doi.org/10.1007/978-3-030-33843-5_7

Johnson PM, Muckley MJ, Bruno M, Kobler E, Hammernik K, Pock T et al. Joint Multi-anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions. In Knoll F, Maier A, Rueckert D, Ye J, editors, Machine Learning for Medical Image Reconstruction: Second International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings. Cham: Springer. 2019. p. 71-79. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-33843-5_7

Johnson, Patricia M ; Muckley, Matthew J ; Bruno, Mary et al. / Joint Multi-anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions. Machine Learning for Medical Image Reconstruction: Second International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings. editor / F. Knoll ; A. Maier ; D. Rueckert ; J. Ye. Cham : Springer, 2019. pp. 71-79 (Lecture Notes in Computer Science).

@inproceedings{0a9aac068fa1476fa60528079cd96c65,

title = "Joint Multi-anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions",

abstract = "Magnetic resonance imaging is a leading image modality for many clinical applications; however, a significant drawback is the lengthy data acquisition. This motivates the development of methods for reconstruction of sparsely sampled image data. One such technique is the Variational Network (VN), a machine learning method that generalizes traditional iterative reconstruction techniques, learning the regularization term from large amounts of image data. Previously, with the VN technique, reconstruction of 4-fold accelerated knee images was shown to be highly successful. In this work we extend the VN approach to applications beyond knee imaging and evaluate the classic VN and a newly developed Unet-VN in 5 different anatomical regions. We evaluate the networks trained individually for each anatomical area as well as jointly trained with data from all anatomical areas. The VN and Unet-VN were …",

author = "Johnson, {Patricia M} and Muckley, {Matthew J} and Mary Bruno and Erich Kobler and Kerstin Hammernik and Thomas Pock and Florian Knoll",

year = "2019",

doi = "10.1007/978-3-030-33843-5_7",

language = "English",

isbn = "978-3-030-33842-8",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "71--79",

editor = "F. Knoll and A. Maier and D. Rueckert and J. Ye",

booktitle = "Machine Learning for Medical Image Reconstruction",

note = "2019 International Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2019 ; Conference date: 17-10-2019",

}

TY - GEN

T1 - Joint Multi-anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions

AU - Johnson, Patricia M

AU - Muckley, Matthew J

AU - Bruno, Mary

AU - Kobler, Erich

AU - Hammernik, Kerstin

AU - Pock, Thomas

AU - Knoll, Florian

PY - 2019

Y1 - 2019

N2 - Magnetic resonance imaging is a leading image modality for many clinical applications; however, a significant drawback is the lengthy data acquisition. This motivates the development of methods for reconstruction of sparsely sampled image data. One such technique is the Variational Network (VN), a machine learning method that generalizes traditional iterative reconstruction techniques, learning the regularization term from large amounts of image data. Previously, with the VN technique, reconstruction of 4-fold accelerated knee images was shown to be highly successful. In this work we extend the VN approach to applications beyond knee imaging and evaluate the classic VN and a newly developed Unet-VN in 5 different anatomical regions. We evaluate the networks trained individually for each anatomical area as well as jointly trained with data from all anatomical areas. The VN and Unet-VN were …

AB - Magnetic resonance imaging is a leading image modality for many clinical applications; however, a significant drawback is the lengthy data acquisition. This motivates the development of methods for reconstruction of sparsely sampled image data. One such technique is the Variational Network (VN), a machine learning method that generalizes traditional iterative reconstruction techniques, learning the regularization term from large amounts of image data. Previously, with the VN technique, reconstruction of 4-fold accelerated knee images was shown to be highly successful. In this work we extend the VN approach to applications beyond knee imaging and evaluate the classic VN and a newly developed Unet-VN in 5 different anatomical regions. We evaluate the networks trained individually for each anatomical area as well as jointly trained with data from all anatomical areas. The VN and Unet-VN were …

U2 - 10.1007/978-3-030-33843-5_7

DO - 10.1007/978-3-030-33843-5_7

M3 - Conference paper

SN - 978-3-030-33842-8

T3 - Lecture Notes in Computer Science

SP - 71

EP - 79

BT - Machine Learning for Medical Image Reconstruction

A2 - Knoll, F.

A2 - Maier, A.

A2 - Rueckert, D.

A2 - Ye, J.

PB - Springer

CY - Cham

T2 - 2019 International Workshop on Machine Learning for Medical Image Reconstruction

Y2 - 17 October 2019

ER -

Joint Multi-anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions

Abstract

Publication series

Conference

Access to Document

Fingerprint

Cite this