560-04-002 ISMRM Abstract

Contrast-Agnostic Deep-Learning Segmentation of Free-Running Whole-Heart MRI

Primary: Analysis Methods - Segmentation and Detection

Secondary: Cardiovascular - Myocardium

560-04-002 · Automated Cardiovascular MRI · Wednesday, 13 May, 2:35 PM–3:30 PM · Digital Posters Row A

Keywords: Cardiac MRI Contrast-agnostic Free-running MRI Whole-heart MRI Deep Learning Segmentation

Accepted

Léonard Treil¹, Shuailong Zhu¹, Angela Rocca², Jérôme Yerly^1,3, Robert J Holtackers, Matthias Stuber^1,3, Roger Hullin², David Rotzinger¹, Augustin C Ogier¹, Ruud B van Heeswijk ⁴

¹Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland

²Cardiovascular Department, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland

³CIBM Center for BioMedical Imaging, Lausanne, Switzerland, Switzerland

⁴Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland

Presenting Author: Ruud B van Heeswijk

Synopsis

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References

1. Di Sopra L, Piccini D, Coppo S, Stuber M, Yerly J. An automated approach to fully self‐gated free‐running cardiac and respiratory motion‐resolved 5D whole‐heart MRI. Magn Reson Med. 2019;82(6):2118-2132. doi:10.1002/mrm.27898 [doi]

2. Ogier AC, Baup S, Ilanjian G, et al. Cardiac Function Assessment with Deep-Learning-Based Automatic Segmentation of Free-Running 4D Whole-Heart CMR. Radiology and Imaging. Preprint posted online July 17, 2025. doi:10.1101/2025.07.15.25331281 [doi]

3. Billot B, Greve DN, Puonti O, et al. SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining. Med Image Anal. 2023;86:102789. doi:10.1016/j.media.2023.102789 [doi]

4. Holtackers RJ, Ogier AC, Romanin L, et al. How low can we go? The effect of acquisition duration on cardiac volume and function measurements in free-running cardiac and respiratory motion-resolved five-dimensional whole-heart cine magnetic resonance imaging at 1.5T. J Cardiovasc Magn Reson. 2025;27(1):101863. doi:10.1016/j.jocmr.2025.101863 [doi]

5. Meyer P, Rocca A, Banus J, et al. The HeartMagic prospective observational study protocol – characterizing subtypes of heart failure with preserved ejection fraction. Cardiovascular Medicine. Preprint posted online April 10, 2025. doi:10.1101/2025.04.10.25325567 [doi]

6. Isensee F, Rokuss M, Krämer L, et al. nnInteractive: Redefining 3D Promptable Segmentation. arXiv. Preprint posted online 2025. doi:10.48550/ARXIV.2503.08373 [doi]

7. Tustison NJ, Avants BB. Explicit B-spline regularization in diffeomorphic image registration. Front Neuroinformatics. 2013;7. doi:10.3389/fninf.2013.00039 [doi]

8. Kawel-Boehm N, Hetzel SJ, Ambale-Venkatesh B, et al. Society for Cardiovascular Magnetic Resonance reference values (“normal values”) in cardiovascular magnetic resonance: 2025 update. J Cardiovasc Magn Reson. 2025;27(1):101853. doi:10.1016/j.jocmr.2025.101853 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/560-04-002