365-02-006 ISMRM Abstract

One-click AI-assisted analysis of joint bright- and black-blood LGE and T2 mapping in acute STEMI patients

Primary: Cardiovascular - Myocardium

Secondary: Analysis Methods - Segmentation and Detection

365-02-006 · Post-Processing of Cardiovascular MRI · Monday, 11 May, 9:15 AM–10:10 AM · Digital Posters Row F

Keywords: Machine Learning/Artificial Intelligence Cardiac MRI Quantitative myocardial mapping Rapid imaging Co-registered

Accepted

Victor de Villedon de Naide ^1,2, Edouard Gerbaud^1,3, Sane Viola^1,2, Thaïs Génisson^1,2, Kalvin Narceau^1,2, Ewan Barel^1,2, Théo Richard^1,2, Pierre Jaïs^1,4, Matthias Stuber^1,5,6, Hubert Cochet^1,2, Aurelien Bustin^1,2,5

¹IHU LIRYC, Heart Rhythm Disease Institute, Bordeaux, France

²Department of Cardiovascular Imaging, Hôpital Cardiologique du Haut-Lévêque, Bordeaux, France

³Cardiology Intensive Care Unit, Hôpital Cardiologique du Haut-Lévêque, Bordeaux, France

⁴Hôpital Cardiologique du Haut-Lévêque, Bordeaux, France

⁵Department of Diagnostic and Interventional Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland

⁶Center for Biomedical Imaging (CIBM), Lausanne, Switzerland

Presenting Author: Victor de Villedon de Naide

Synopsis

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References

1. Reinstadler SJ, Thiele H, Eitel I. Risk stratification by cardiac magnetic resonance imaging after ST-elevation myocardial infarction. Current Opinion in Cardiology. 2015;30(6):681-689. doi:10.1097/HCO.0000000000000227 [doi]

2. Ibanez B, Aletras AH, Arai AE, et al. Cardiac MRI Endpoints in Myocardial Infarction Experimental and Clinical Trials. Journal of the American College of Cardiology. 2019;74(2):238-256. doi:10.1016/j.jacc.2019.05.024 [doi]

3. Bustin A, de Villedon de Naide V, Gerbaud E, et al. One-click co-registered T2 mapping and dual black- and bright-blood late gadolinium enhancement MRI for comprehensive assessment of myocardial injury after acute STEMI. European Heart Journal - Imaging Methods and Practice. Published online August 11, 2025:qyaf103. doi:10.1093/ehjimp/qyaf103 [doi]

4. Xiao H, Li L, Liu Q, Zhu X, Zhang Q. Transformers in medical image segmentation: A review. Biomedical Signal Processing and Control. 2023;84:104791. doi:10.1016/j.bspc.2023.104791 [doi]

5. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention. Published online 2015:234-241. doi:10.1007/978-3-319-24574-4_28 [doi]

6. Richard T, De Villedon De Naide V, Nogues V, et al. Improved and Automated Detection of Papillary Muscle Infarction Using Joint Bright‐ and Black‐Blood Late Gadolinium Enhancement MRI. J Magn Reson Imaging. 2025;62(3):827-839. doi:10.1002/jmri.29777 [doi]

7. Kellman P, Arai AE, McVeigh ER, Aletras AH. Phase-sensitive inversion recovery for detecting myocardial infarction using gadolinium-delayed hyperenhancement. Magnetic resonance in medicine. 2002;47(2):372-383. doi:10.1002/mrm.10051 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/365-02-006