Detection and correction of cardiac MRI motion artefacts during reconstruction from k-space

Ilkay Oksuz; James Clough; Bram Ruijsink; Esther Puyol-Antón; Aurelien Bustin; Gastao Cruz; Claudia Prieto; Daniel Rueckert; Andrew P. King; Julia A. Schnabel

doi:10.1007/978-3-030-32251-9_76

Detection and correction of cardiac MRI motion artefacts during reconstruction from k-space

Ilkay Oksuz^*
, James Clough
, Bram Ruijsink
, Esther Puyol-Antón
, Aurelien Bustin
, Gastao Cruz
, Claudia Prieto
, Daniel Rueckert
, Andrew P. King
, Julia A. Schnabel

^*Corresponding author for this work

King's College London
School of Biomedical Engineering and Imaging Sciences
Guy's and St Thomas' NHS Foundation Trust
Imperial College London

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

17 Citations (Scopus)

Abstract

In fully sampled cardiac MR (CMR) acquisitions, motion can lead to corruption of k-space lines, which can result in artefacts in the reconstructed images. In this paper, we propose a method to automatically detect and correct motion-related artefacts in CMR acquisitions during reconstruction from k-space data. Our correction method is inspired by work on undersampled CMR reconstruction, and uses deep learning to optimize a data-consistency term for under-sampled k-space reconstruction. Our main methodological contribution is the addition of a detection network to classify motion-corrupted k-space lines to convert the problem of artefact correction to a problem of reconstruction using the data consistency term. We train our network to automatically correct for motion-related artefacts using synthetically corrupted cine CMR k-space data as well as uncorrupted CMR images. Using a test set of 50 2D+time cine CMR datasets from the UK Biobank, we achieve good image quality in the presence of synthetic motion artefacts. We quantitatively compare our method with a variety of techniques for recovering good image quality and showcase better performance compared to state of the art denoising techniques with a PSNR of 37.1. Moreover, we show that our method preserves the quality of uncorrupted images and therefore can be also utilized as a general image reconstruction algorithm.

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	695-703
Number of pages	9
ISBN (Print)	9783030322502
DOIs	https://doi.org/10.1007/978-3-030-32251-9_76
Publication status	Published - 2019
Externally published	Yes
Event	22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China Duration: 13 Oct 2019 → 17 Oct 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11767 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	13/10/19 → 17/10/19

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2019.

Funding

Acknowledgments. This work was supported by an EPSRC programme Grant (EP/P001009/1) and the Wellcome EPSRC Centre for Medical Engineering at the School of Biomedical Engineering and Imaging Sciences, King’s College London (WT 203148/Z/16/Z). This research has been conducted using the UK Biobank Resource under Application Number 17806. The GPU used in this research was generously donated by the NVIDIA Corporation. This work was supported by an EPSRC programme Grant (EP/P001009/1) and the Wellcome EPSRC Centre for Medical Engineering at the School of Biomedical Engineering and Imaging Sciences,King?s College London (WT 203148/Z/16/Z). This research has been conducted using the UK Biobank Resource under Application Number 17806. The GPU used in this research was generously donated by the NVIDIA Corporation.

Funders	Funder number
King?s College London	17806
Wellcome EPSRC
Wellcome EPSRC Centre for Medical Engineering at the School of Biomedical Engineering and Imaging Sciences
King’s College London	WT 203148/Z/16/Z
Engineering and Physical Sciences Research Council	EP/P001009/1

Keywords

Cardiac MR
Convolutional neural networks
Image reconstruction
Motion artefacts
UK Biobank

Access to Document

10.1007/978-3-030-32251-9_76

Cite this

Oksuz, I., Clough, J., Ruijsink, B., Puyol-Antón, E., Bustin, A., Cruz, G., Prieto, C., Rueckert, D., King, A. P., & Schnabel, J. A. (2019). Detection and correction of cardiac MRI motion artefacts during reconstruction from k-space. 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. 695-703). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11767 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-32251-9_76

Oksuz, Ilkay ; Clough, James ; Ruijsink, Bram et al. / Detection and correction of cardiac MRI motion artefacts during reconstruction from k-space. 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. 695-703 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{600b20e0c16b471da7e042212a17ed91,

title = "Detection and correction of cardiac MRI motion artefacts during reconstruction from k-space",

abstract = "In fully sampled cardiac MR (CMR) acquisitions, motion can lead to corruption of k-space lines, which can result in artefacts in the reconstructed images. In this paper, we propose a method to automatically detect and correct motion-related artefacts in CMR acquisitions during reconstruction from k-space data. Our correction method is inspired by work on undersampled CMR reconstruction, and uses deep learning to optimize a data-consistency term for under-sampled k-space reconstruction. Our main methodological contribution is the addition of a detection network to classify motion-corrupted k-space lines to convert the problem of artefact correction to a problem of reconstruction using the data consistency term. We train our network to automatically correct for motion-related artefacts using synthetically corrupted cine CMR k-space data as well as uncorrupted CMR images. Using a test set of 50 2D+time cine CMR datasets from the UK Biobank, we achieve good image quality in the presence of synthetic motion artefacts. We quantitatively compare our method with a variety of techniques for recovering good image quality and showcase better performance compared to state of the art denoising techniques with a PSNR of 37.1. Moreover, we show that our method preserves the quality of uncorrupted images and therefore can be also utilized as a general image reconstruction algorithm.",

keywords = "Cardiac MR, Convolutional neural networks, Image reconstruction, Motion artefacts, UK Biobank",

author = "Ilkay Oksuz and James Clough and Bram Ruijsink and Esther Puyol-Ant{\'o}n and Aurelien Bustin and Gastao Cruz and Claudia Prieto and Daniel Rueckert and King, \{Andrew P.\} and Schnabel, \{Julia A.\}",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 ; Conference date: 13-10-2019 Through 17-10-2019",

year = "2019",

doi = "10.1007/978-3-030-32251-9\_76",

language = "English",

isbn = "9783030322502",

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

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "695--703",

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

booktitle = "Medical Image Computing and Computer Assisted Intervention {\textendash} MICCAI 2019 - 22nd International Conference, Proceedings",

address = "Germany",

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Oksuz, I, Clough, J, Ruijsink, B, Puyol-Antón, E, Bustin, A, Cruz, G, Prieto, C, Rueckert, D, King, AP & Schnabel, JA 2019, Detection and correction of cardiac MRI motion artefacts during reconstruction from k-space. 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. 11767 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 695-703, 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, China, 13/10/19. https://doi.org/10.1007/978-3-030-32251-9_76

Detection and correction of cardiac MRI motion artefacts during reconstruction from k-space. / Oksuz, Ilkay; Clough, James; Ruijsink, Bram 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. 695-703 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11767 LNCS).

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

TY - GEN

T1 - Detection and correction of cardiac MRI motion artefacts during reconstruction from k-space

AU - Oksuz, Ilkay

AU - Clough, James

AU - Ruijsink, Bram

AU - Puyol-Antón, Esther

AU - Bustin, Aurelien

AU - Cruz, Gastao

AU - Prieto, Claudia

AU - Rueckert, Daniel

AU - King, Andrew P.

AU - Schnabel, Julia A.

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019

Y1 - 2019

N2 - In fully sampled cardiac MR (CMR) acquisitions, motion can lead to corruption of k-space lines, which can result in artefacts in the reconstructed images. In this paper, we propose a method to automatically detect and correct motion-related artefacts in CMR acquisitions during reconstruction from k-space data. Our correction method is inspired by work on undersampled CMR reconstruction, and uses deep learning to optimize a data-consistency term for under-sampled k-space reconstruction. Our main methodological contribution is the addition of a detection network to classify motion-corrupted k-space lines to convert the problem of artefact correction to a problem of reconstruction using the data consistency term. We train our network to automatically correct for motion-related artefacts using synthetically corrupted cine CMR k-space data as well as uncorrupted CMR images. Using a test set of 50 2D+time cine CMR datasets from the UK Biobank, we achieve good image quality in the presence of synthetic motion artefacts. We quantitatively compare our method with a variety of techniques for recovering good image quality and showcase better performance compared to state of the art denoising techniques with a PSNR of 37.1. Moreover, we show that our method preserves the quality of uncorrupted images and therefore can be also utilized as a general image reconstruction algorithm.

AB - In fully sampled cardiac MR (CMR) acquisitions, motion can lead to corruption of k-space lines, which can result in artefacts in the reconstructed images. In this paper, we propose a method to automatically detect and correct motion-related artefacts in CMR acquisitions during reconstruction from k-space data. Our correction method is inspired by work on undersampled CMR reconstruction, and uses deep learning to optimize a data-consistency term for under-sampled k-space reconstruction. Our main methodological contribution is the addition of a detection network to classify motion-corrupted k-space lines to convert the problem of artefact correction to a problem of reconstruction using the data consistency term. We train our network to automatically correct for motion-related artefacts using synthetically corrupted cine CMR k-space data as well as uncorrupted CMR images. Using a test set of 50 2D+time cine CMR datasets from the UK Biobank, we achieve good image quality in the presence of synthetic motion artefacts. We quantitatively compare our method with a variety of techniques for recovering good image quality and showcase better performance compared to state of the art denoising techniques with a PSNR of 37.1. Moreover, we show that our method preserves the quality of uncorrupted images and therefore can be also utilized as a general image reconstruction algorithm.

KW - Cardiac MR

KW - Convolutional neural networks

KW - Image reconstruction

KW - Motion artefacts

KW - UK Biobank

UR - https://www.scopus.com/pages/publications/85075664999

U2 - 10.1007/978-3-030-32251-9_76

DO - 10.1007/978-3-030-32251-9_76

M3 - Conference contribution

AN - SCOPUS:85075664999

SN - 9783030322502

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

SP - 695

EP - 703

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings

A2 - Shen, Dinggang

A2 - Yap, Pew-Thian

A2 - Liu, Tianming

A2 - Peters, Terry M.

A2 - Khan, Ali

A2 - Staib, Lawrence H.

A2 - Essert, Caroline

A2 - Zhou, Sean

PB - Springer Science and Business Media Deutschland GmbH

T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019

Y2 - 13 October 2019 through 17 October 2019

ER -

Oksuz I, Clough J, Ruijsink B, Puyol-Antón E, Bustin A, Cruz G et al. Detection and correction of cardiac MRI motion artefacts during reconstruction from k-space. 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. 695-703. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-32251-9_76

Detection and correction of cardiac MRI motion artefacts during reconstruction from k-space

Abstract

Publication series

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Keywords

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