Abstract
Artefacts constitute a paramount issue in medical imaging where the prevalence of artefacts may severely impact the clinical diagnosis accuracy. Specifically, the mistriggering and motion family of artefacts during the cardiac MR image acquisition would eventually damage the visibility of certain tissues, such as the left ventricular, right ventricular, and myocardium. This would cause the ejection fraction to be incorrectly estimated and the patient’s heart condition to be incorrectly evaluated. Despite much research on medical image reconstruction, relatively little work has been done for cardiac MRI artefact correction. In this work, inspired by the image reconstruction literature, we propose to use an auto-encoder-guided mistriggering artefact correction method, which not only corrects the artefacts in the image domain but also in the k-space domain with the introduction of an enhanced structure. We conduct a variety of experiments on photos and medical images to compare the performances of different network architectures under mistriggering artefacts and gaussian noise. We demonstrate the superiority of the cross-domain network in the case of k-space-related artefacts.
Original language | English |
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Title of host publication | Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge - 12th International Workshop, STACOM 2021, Held in Conjunction with MICCAI 2021, Revised Selected Papers |
Editors | Esther Puyol Antón, Alistair Young, Avan Suinesiaputra, Mihaela Pop, Carlos Martín-Isla, Maxime Sermesant, Oscar Camara, Karim Lekadir |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 199-207 |
Number of pages | 9 |
ISBN (Print) | 9783030937218 |
DOIs | |
Publication status | Published - 2022 |
Event | 12th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2021 held in conjunction with MICCAI 2021 - Strasbourg, France Duration: 27 Sept 2021 → 27 Sept 2021 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 13131 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 12th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2021 held in conjunction with MICCAI 2021 |
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Country/Territory | France |
City | Strasbourg |
Period | 27/09/21 → 27/09/21 |
Bibliographical note
Publisher Copyright:© 2022, Springer Nature Switzerland AG.
Keywords
- Auto-encoders
- Cardiac MRI
- Image artefacts
- Image denoising