Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
368-01-004 ISMRM Abstract

Population-Level 4D Cardiac Atlas for Deep Phenotyping and Cardiovascular Disease Diagnosis

Primary: Cardiovascular - Myocardium
Secondary: Analysis Methods - Classification and Prediction
368-01-004 · Registration, Atlases, and Motion · Monday, 11 May, 8:20 AM–9:15 AM · Digital Posters Row I
Keywords: Diagnosis/Prediction Magnetic resonance imaging Cardiac Motion Cardiac atlas Deep Phenotyping
Accepted
Guangming Wang 1, Qirong Li2, Lizhen Lan1, Yajing Zhang3, Mo Yang1, Qing Li1, Tianxing He1, Yan Li4, Chengyan Wang1
1Human Phenome Institute, Shanghai, China
2College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China
3Science & Technology, Beijing, China
4Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Presenting Author: Guangming Wang

Synopsis

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References

1. Shenoy C, Grizzard JD, Shah DJ, Kassi M, Reardon MJ, Zagurovskaya M, Kim HW, Parker MA, Kim RJ. Cardiovascular magnetic resonance imaging in suspected cardiac tumour: a multicentre outcomes study. European heart journal. 2022 Jan 1;43(1):71-80.
2. Rajiah PS, François CJ, Leiner T. Cardiac MRI: state of the art. Radiology. 2023 Apr 11;307(3):e223008.
3. Burns R, Young W J, Aung N, et al. Genetic basis of right and left ventricular heart shape[J]. Nature communications, 2024, 15(1): 9437.
4. Marciniak M, van Deutekom A W, Toemen L, et al. A three-dimensional atlas of child’s cardiac anatomy and the unique morphological alterations associated with obesity[J]. European Heart Journal-Cardiovascular Imaging, 2022, 23(12): 1645-1653.
5. Govil S, Crabb B T, Deng Y, et al. A deep learning approach for fully automated cardiac shape modeling in tetralogy of Fallot[J]. Journal of Cardiovascular Magnetic Resonance, 2023, 25(1): 15.
6. Perperidis D, Mohiaddin R, Rueckert D. Construction of a 4D statistical atlas of the cardiac anatomy and its use in classification[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005: 402-410.
7. Qiao M, Zheng J, Zhang W, et al. Mesh4D: A Motion-Aware Multi-view Variational Autoencoder for 3D+ t Mesh Reconstruction[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2025: 343-353.
8. Durrleman S, Prastawa M, Charon N, et al. Morphometry of anatomical shape complexes with dense deformations and sparse parameters[J]. NeuroImage, 2014, 101: 35-49.
9. Isensee F, Jaeger P F, Kohl S A A, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation[J]. Nature methods, 2021, 18(2): 203-211.
10. Ugurlu D, Qian S, Fairweather E, et al. Cardiac digital twins at Scale from MRI: open tools and representative models from~ 55000 UK Biobank participants[J]. Plos one, 2025, 20(7): e0327158.
11. Qi C R, Su H, Mo K, et al. Pointnet: Deep learning on point sets for 3d classification and segmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 652-660.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/368-01-004