Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
507-02-004 ISMRM Abstract

A Multi-Task Diffusion Framework for Synthetic Contrast-Free LGE and Simultaneous Myocardial Infarction Segmentation

Primary: Analysis Methods - Generative Models
Secondary: Cardiovascular - Myocardium
507-02-004 · Generative Modeling: A Unifying Lens on MRI · Wednesday, 13 May, 8:20 AM–10:10 AM · Meeting Room 1.40
Keywords: Generative diffusion model Myocardial infarction Multi-task Learning Virtual LGE
Accepted
Jing Qi1, Xiuzheng Yue 1,2, Miao Hu1, Yinyin Chen3, Hang Jin3, Tao Li4, Kunlun He1
1Medical Big Data Research Center, Medical Innovation Research Division, Chinese PLA General Hospital, Beijing, China
2Clinical and Technical Support, Philips Healthcare (Beijing), Beijing, China
3Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
4The First Medical Center, Chinese PLA General Hospital, Beijing, China
Presenting Author: Xiuzheng Yue

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References

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2. Holtackers RJ, Emrich T, Botnar RM, et al. Late gadolinium enhancement cardiac magnetic resonance imaging: From basic concepts to emerging methods. Rofo. 2022;194(5):491-504. PMID: 35196714 DOI: 10.1055/a-1718-4355 [doi] [pmid]
3. Zhang Q, Burrage MK, Lukaschuk E, et al. Toward replacing late gadolinium enhancement with artificial intelligence virtual native enhancement for gadolinium-free cardiovascular magnetic resonance tissue characterization in hypertrophic cardiomyopathy. Circulation. 2021;144(8):589-599. PMID: 34229451 DOI: 10.1161/CIRCULATIONAHA.121.054432 [doi] [pmid]
4. Zhang Q, Burrage MK, Shanmuganathan M, et al. Artificial intelligence for contrast-free MRI: Scar assessment in myocardial infarction using deep learning-based virtual native enhancement. Circulation. 2022;146(20):1492-1503. PMID: 36124774 DOI: 10.1161/CIRCULATIONAHA.122.060137 [doi] [pmid]
5. Qi H, Qian P, Tang L, et al. Predicting late gadolinium enhancement of acute myocardial infarction in contrast-free cardiac cine MRI using deep generative learning. Circ Cardiovasc Imaging. 2024;17(9):e016786. PMID: 39253820 DOI: 10.1161/CIRCIMAGING.124.016786 [doi] [pmid]
6. Qi R, Tao M, Xu C, et al. Knowledge-driven interpretative conditional diffusion model for contrast-free myocardial infarction enhancement synthesis. Med Image Anal. 2025;105:103701. PMID: 40644917 DOI: 10.1016/j.media.2025.103701 [doi] [pmid]
7. Radford A, Kim JW, Hallacy C, et al. Learning transferable visual models from natural language supervision. Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event. 2021;139:8748-8763. URL: http://proceedings.mlr.press/v139/radford21a.html

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/507-02-004