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Cardiac mr motion artefact correction from k-space using deep learning-based reconstruction

Oksuz I, Clough J, Bustin A, Cruz G, Prieto C, Botnar R, Rueckert D, Schnabel JA, King AP (2018)

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Publication Type: Conference contribution

Publication year: 2018

Journal

Publisher: Springer Verlag

Book Volume: 11074 LNCS

Pages Range: 21-29

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Granada, ESP

ISBN: 9783030001285

DOI: 10.1007/978-3-030-00129-2_3

Abstract

Incorrect ECG gating of cardiac magnetic resonance (CMR) acquisitions can lead to artefacts, which hampers the accuracy of diagnostic imaging. Therefore, there is a need for robust reconstruction methods to ensure high image quality. In this paper, we propose a method to automatically correct motion-related artefacts in CMR acquisitions during reconstruction from k-space data. Our method is based on the Automap reconstruction method, which directly reconstructs high quality MR images from k-space using deep learning. Our main methodological contribution is the addition of an adversarial element to this architecture, in which the quality of image reconstruction (the generator) is increased by using a discriminator. We train the reconstruction network to automatically correct for motion-related artefacts using synthetically corrupted CMR k-space data and uncorrupted reconstructed images. Using 25000 images from the UK Biobank dataset we achieve good image quality in the presence of synthetic motion artefacts, but some structural information was lost. We quantitatively compare our method to a standard inverse Fourier reconstruction. In addition, we qualitatively evaluate the proposed technique using k-space data containing real motion artefacts.

Involved external institutions

How to cite

APA:

Oksuz, I., Clough, J., Bustin, A., Cruz, G., Prieto, C., Botnar, R.,... King, A.P. (2018). Cardiac mr motion artefact correction from k-space using deep learning-based reconstruction. In Florian Knoll, Andreas Maier, Daniel Rueckert (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 21-29). Granada, ESP: Springer Verlag.

MLA:

Oksuz, Ilkay, et al. "Cardiac mr motion artefact correction from k-space using deep learning-based reconstruction." Proceedings of the 1st Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2018 Held in Conjunction with 21st Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, ESP Ed. Florian Knoll, Andreas Maier, Daniel Rueckert, Springer Verlag, 2018. 21-29.

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