Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
570-06-211 ISMRM Abstract

A Diffusion Model with Multi-task Learning for Diagnosing Acute Myocardial Infarction from Non-contrast Cardiac Cine MR

Primary: Cardiovascular
Secondary: Physics & Engineering
570-06-211 · Vision and Language Models in MRI · Wednesday, 13 May, 2:35 PM–3:30 PM · Traditional Posters | Exhibition Hall
Keywords: Cardiac MRI Generative diffusion model Medical imae synthesis Microvascular obstruction Acute Myocardial Infarction (AMI)
Accepted
YIMING ZHU1,2, Hanxi Liao 1,2, Dongaolei An3, Lian-Ming Wu4,5,6, Haikun Qi1,2,7,8,9,10,11,12,13
1School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
2State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
3Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, China
4Department of Radiology, Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital, Shanghai, China
5Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital, Shanghai, China
6Department of Radiology, Department of Radiology, Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital, Shanghai, China
7ShanghaiTech University, Shanghai, China
8Shanghai Clinical Trial Center, Shanghai, China
9School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
10Shanghai Clinical Research and Trial Center, shanghai, China
11School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
12Shanghai Clinical Research and Trial Center, Shanghai, China
13Shanghai Clinical Research and Trial Center, ShanghaiTech University, shanghai, China
Presenting Author: Hanxi Liao

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References

1. Beijnink C , Hoeven N V D , Konijnenberg L ,et al.Cardiac MRI to Visualize Myocardial Damage after ST-Segment Elevation Myocardial Infarction: A Review of Its Histologic Validation.[J].Radiology, 2021, 301(1):4-18.DOI:10.1148/radiol.2021204265. [doi]
2. ZHANG Q, BURRAGE M K, SHANMUGANATHAN M, et al., 2022. Artificial Intelligence for Contrast-Free MRI: Scar Assessment in Myocardial Infarction Using Deep Learning – Based Virtual Native Enhancement[J]. Circulation, 146(20): 1492-1503. DOI: 10.1161/CIRCULATIONAHA.122.060137. [doi]
3. Qi R , Li X , Xu L ,et al.Cardiac Physiology Knowledge-Driven Diffusion Model forContrast-Free Synthesis Myocardial Infarction Enhancement[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention.Springer, Cham, 2024.DOI:10.1007/978-3-031-72378-0_19. [doi]
4. 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[J].Circulation. Cardiovascular imaging, 17(9). DOI:10.1161/CIRCIMAGING.124.016786. [doi]
5. Ho J, Salimans T, Gritsenko A, et al. Video diffusion models. Advances in Neural Information Processing Systems, 2022, 35: 8633-8646. https://doi.org/10.48550/arXiv.2204.03458 [doi]
6. GRÄNI C, EICHHORN C, BIÈRE L, et al., 2019. Comparison of Myocardial Fibrosis Quantification Methods by Cardiovascular Magnetic Resonance Imaging for Risk Stratification of Patients with Suspected Myocarditis[J]. Journal of Cardiovascular Magnetic Resonance, 21(1): 14. DOI: 10.1186/s12968-019-0520-0. [doi]
7. AMADO L C, GERBER B L, GUPTA S N, et al., 2004. Accurate and Objective Infarct Sizing by Contrast-Enhanced Magnetic Resonance Imaging in a Canine Myocardial Infarction Model[J]. Journal of the American College of Cardiology, 44(12): 2383-2389. DOI: 10.1016/j.jacc.2004.0 9.020 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/570-06-211