Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
503-03-009 ISMRM Abstract

Simulation of MRI K-Space Motion Corruption for Deep Learning-Based Artifact Detection and Correction

Accepted
Kathryn E Lamar-Bruno 1, Samira Masoudi2, Brendan Crabb3, Amin Mahmoodi1, Alta Steward1, Albert Hsiao3
1Department of Bioengineering, University of California, San Diego, United States of America
2Halıcıoğlu Data Science Institute, University of California, San Diego, United States of America
3Department of Radiology, University of California, San Diego, United States of America
Presenting Author: Kathryn E Lamar-Bruno

Synopsis

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References

1. Oksuz, I., et al. (2019). Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning. Medical image analysis, 55, 136–147. https://doi.org/10.1016/j.media.2019.04.009 [doi]
2. Lorch, et al. (2017). Automated Detection of Motion Artefacts in MR Imaging Using Decision Forests, Journal of Medical Engineering, 2017, 4501647, 9 pages https://doi.org/10.1155/2017/4501647 [doi]
3. Lei, K.,et al. (2022). Artifact- and content-specific quality assessment for MRI with image rulers. Medical image analysis, 77, 102344. https://doi.org/10.1016/j.media.2021.102344 [doi]
4. McCormick M, et al. ITK: enabling reproducible research and open science. Front Neuroinform. 2014;8:13. Published 2014 Feb 20. doi:10.3389/fninf.2014.00013 [doi]
5. Yoo TS, et al. Engineering and Algorithm Design for an Image Processing API: A Technical Report on ITK – The Insight Toolkit. In Proc. of Medicine Meets Virtual Reality, J. Westwood, ed., IOS Press Amsterdam pp 586-592 (2002).

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