Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
401-02-007 ISMRM Abstract

KIMRA: K-space–Image Multimodal Representation Alignment for Comprehensive Cardiac Analysis

Primary: Analysis Methods - Foundation Models
Secondary: Analysis Methods - Classification and Prediction
401-02-007 · Foundation Models · Tuesday, 12 May, 8:20 AM–10:10 AM · Hall 1A
Keywords: Segmentation Cardiac Cine MRI Representation learning K-space mearsurements Phenotype prediction
Accepted
Yundi Zhang 1, Sevgi Gokce Kafali1, Daniel Rueckert1,2,3, Jiazhen Pan1
1Chair for AI in Healthcare and Medicine, Technical University of Munich and TUM University Hospital, Munich, Germany
2Department of Computing, Imperial College London, London, United Kingdom
3Munich Center for Machine Learning (MCML), München, Germany
Presenting Author: Yundi Zhang

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References

1. Petersen, Steffen E., et al. "UK Biobank's cardiovascular magnetic resonance protocol." Journal of cardiovascular magnetic resonance 18.1 (2016): 8.
2. Pal, Arghya, and Yogesh Rathi. "A review and experimental evaluation of deep learning methods for MRI reconstruction." The journal of machine learning for biomedical imaging 1 (2022): 001.
3. Zhang, Yundi, et al. "Towards cardiac mri foundation models: Comprehensive visual-tabular representations for whole-heart assessment and beyond." arXiv preprint arXiv:2504.13037 (2025).
4. Chen, Chen, et al. "Deep learning for cardiac image segmentation: a review." Frontiers in cardiovascular medicine 7 (2020): 25.
5. He, Kaiming, et al. "Masked autoencoders are scalable vision learners." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.
6. Zhang, Yundi, et al. "Towards cardiac mri foundation models: Comprehensive visual-tabular representations for whole-heart assessment and beyond." arXiv preprint arXiv:2504.13037 (2025).
7. Radford, Alec, et al. "Learning transferable visual models from natural language supervision." International conference on machine learning. PmLR, 2021.
8. Dosovitskiy, Alexey. "An image is worth 16x16 words: Transformers for image recognition at scale." arXiv preprint arXiv:2010.11929 (2020).
9. He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
10. Isensee, Fabian, et al. "nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation." Nature methods 18.2 (2021): 203-211.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/401-02-007