Research output: Contribution to journal › Conference paper
Ilkay Oksuz, James Clough, Bram Ruijsink, Esther Puyol-Antón, Aurelien Bustin, Gastao Cruz, Claudia Prieto, Daniel Rueckert, Andrew P. King, Julia A. Schnabel
Original language | English |
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Pages (from-to) | 695-703 |
Number of pages | 9 |
Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Early online date | 10 Oct 2019 |
DOIs | |
Accepted/In press | 5 Jun 2019 |
E-pub ahead of print | 10 Oct 2019 |
Additional links | |
Event | 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China Duration: 13 Oct 2019 → 17 Oct 2019 |
Detection and Correction of_OKSUZ_Accepted5June2019Publishedonline10October2019_GREEN AAM
Detection_and_Correction_of_OKSUZ_Accepted5June2019Publishedonline10October2019_GREEN_AAM.pdf, 545 KB, application/pdf
Uploaded date:11 Dec 2019
Version:Accepted author manuscript
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.
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