Deep Learning-Based Detection and Correction of Cardiac MR Motion Artefacts during Reconstruction for High-Quality Segmentation

Ilkay Oksuz*, James R. Clough, Bram Ruijsink, Esther Puyol Anton, Aurelien Bustin, Gastao Cruz, Claudia Prieto, Andrew P. King, Julia A. Schnabel

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

51 Citations (Scopus)

Abstract

Segmenting anatomical structures in medical images has been successfully addressed with deep learning methods for a range of applications. However, this success is heavily dependent on the quality of the image that is being segmented. A commonly neglected point in the medical image analysis community is the vast amount of clinical images that have severe image artefacts due to organ motion, movement of the patient and/or image acquisition related issues. In this paper, we discuss the implications of image motion artefacts on cardiac MR segmentation and compare a variety of approaches for jointly correcting for artefacts and segmenting the cardiac cavity. The method is based on our recently developed joint artefact detection and reconstruction method, which reconstructs high quality MR images from k-space using a joint loss function and essentially converts the artefact correction task to an under-sampled image reconstruction task by enforcing a data consistency term. In this paper, we propose to use a segmentation network coupled with this in an end-to-end framework. Our training optimises three different tasks: 1) image artefact detection, 2) artefact correction and 3) image segmentation. We train the reconstruction network to automatically correct for motion-related artefacts using synthetically corrupted cardiac MR k-space data and uncorrected reconstructed images. Using a test set of 500 2D+time cine MR acquisitions from the UK Biobank data set, we achieve demonstrably good image quality and high segmentation accuracy in the presence of synthetic motion artefacts. We showcase better performance compared to various image correction architectures.

Original languageEnglish
Article number9139486
Pages (from-to)4001-4010
Number of pages10
JournalIEEE Transactions on Medical Imaging
Volume39
Issue number12
DOIs
Publication statusPublished - Dec 2020

Bibliographical note

Publisher Copyright:
© 1982-2012 IEEE.

Funding

This work was supported in part by the EPSRC Program under Grant EP/P001009/1, in part by the Welcome EPSRC Centre for Medical Engineering at the School of Biomedical Engineering and Imaging Sciences, King's College London under Grant WT 203148/Z/16/Z, and in part by the U.K. Biobank Resource under Grant 17806. The work of Ilkay Oksuz was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant 118C353.

FundersFunder number
TUBITAK118C353
Welcome EPSRC Centre for Medical Engineering at the School of Biomedical Engineering and Imaging Sciences
Engineering and Physical Sciences Research CouncilEP/P001009/1
King's College London17806, WT 203148/Z/16/Z
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu

    Keywords

    • cardiac MRI
    • deep learning
    • image artefacts
    • Image quality
    • image segmentation

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