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
Primary:
Acquisition & Reconstruction - Artifacts and Correction Strategies
Secondary:
Acquisition & Reconstruction - AI methods
503-03-009 · AI: Emerging Techniques and Clinical Applications
· Wednesday, 13 May, 4:00 PM–5:50 PM · Auditorium 1
Keywords:Motion CorrectionImage Quality AssessmentCardiac Cine MRIMotion artifactsDelayed Myocardial Enhancement
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
Motivation:
Goals:
Approach:
Results:
Full abstract & presentation
The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.
Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.
To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.
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).