Research output: Contribution to journal › Article
Ilkay Oksuz, James Clough, Aurelien Bustin, Gastao Cruz, Claudia Prieto, Rene Botnar, Daniel Rueckert, Julia A. Schnabel, Andrew P. King
Original language | English |
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Pages (from-to) | 21-29 |
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 | 12 Sep 2018 |
DOIs | |
Accepted/In press | 8 Jul 2018 |
E-pub ahead of print | 12 Sep 2018 |
Additional links | |
Event | 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, Spain Duration: 16 Sep 2018 → 16 Sep 2018 |
Cardiac MR Motion Artefact_OKSUZ_Firstonline12September2018_GREEN AAM
Cardiac_MR_Motion_Artefact_OKSUZ_Firstonline12September2018_GREEN_AAM.pdf, 334 KB, application/pdf
Uploaded date:08 Nov 2018
Version:Accepted author manuscript
“The final authenticated version is
available online at https://doi.org/10.1007/978-3-030-00129-2_3”
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.
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