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

Oksuz I, Clough JR, Ruijsink B, Anton EP, Bustin A, Cruz G, Prieto C, King AP, Schnabel JA (2020)

Publication Type: Journal article

Publication year: 2020

Journal

IEEE Transactions on Medical Imaging Institute of Electrical and Electronics Engineers (IEEE)

Book Volume: 39

Pages Range: 4001-4010

Article Number: 9139486

Journal Issue: 12

DOI: 10.1109/TMI.2020.3008930

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.

Involved external institutions

Istanbul Technical University / İstanbul Teknik Üniversitesi (İTÜ) TR Turkey (TR) King’s College London GB United Kingdom (GB)

How to cite

APA:

Oksuz, I., Clough, J.R., Ruijsink, B., Anton, E.P., Bustin, A., Cruz, G.,... Schnabel, J.A. (2020). Deep Learning-Based Detection and Correction of Cardiac MR Motion Artefacts during Reconstruction for High-Quality Segmentation. IEEE Transactions on Medical Imaging, 39(12), 4001-4010. https://doi.org/10.1109/TMI.2020.3008930

MLA:

Oksuz, Ilkay, et al. "Deep Learning-Based Detection and Correction of Cardiac MR Motion Artefacts during Reconstruction for High-Quality Segmentation." IEEE Transactions on Medical Imaging 39.12 (2020): 4001-4010.

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