Deep Learning Based Detection and Correction of Cardiac MR Motion
Artefacts During Reconstruction for High-Quality Segmentation
release_qm7yr4dhtzajdgziuxtpsf36ye
by
Ilkay Oksuz, James R. Clough, Bram Ruijsink, Esther Puyol Anton,
Aurelien Bustin, Gastao Cruz, Claudia Prieto, Andrew P. King, Julia A.
Schnabel
2019
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. 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 quantitatively compare our method with a
variety of techniques for jointly recovering image quality and performing image
segmentation. We showcase better performance compared to state-of-the-art image
correction techniques. Moreover, our method preserves the quality of
uncorrupted images and therefore can be utilised as a global image
reconstruction algorithm.
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