Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
508-02-003 ISMRM Abstract

One-Heartbeat Cardiac Cine MRI via Phase-Guided Conditional Diffusion

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Acquisition & Reconstruction - AI methods
508-02-003 · Novel Reconstruction Techniques for Fast Imaging · Wednesday, 13 May, 8:20 AM–10:10 AM · Meeting Room 1.60
Keywords: Diffusion Modeling Real-time MRI Cardiac Cine MRI Conditional Diffusion
Accepted
Hanrui Shi1, Jian Xu2, Qi Liu 2, Hongyu Li2
1Electrical & Computer Engineering, University of Washington, Seattle, United States of America
2United Imaging Healthcare North America, Houston, United States of America
Presenting Author: Qi Liu

Synopsis

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References

1. Küstner, Thomas, et al. "CINENet: deep learning-based 3D cardiac CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions." Scientific reports 10.1 (2020): 13710.
2. Wang, Xiaoqing, Martin Uecker, and Li Feng. "Fast real-time cardiac MRI: a review of current techniques and future directions." Investigative Magnetic Resonance Imaging 25.4 (2021): 252-265.
3. Cruz, Gastao, et al. "Single‐heartbeat cardiac cine imaging via jointly regularized nonrigid motion‐corrected reconstruction." NMR in Biomedicine 36.9 (2023): e4942.
4. El‐Rewaidy, Hossam, et al. "Multi‐domain convolutional neural network (MD‐CNN) for radial reconstruction of dynamic cardiac MRI." Magnetic Resonance in Medicine 85.3 (2021): 1195-1208.
5. Gülle, Merve, Sebastian Weingärtner, and Mehmet Akçakaya. "Deep Learning Assisted Outer Volume Removal for Highly-Accelerated Real-Time Dynamic MRI." arXiv preprint arXiv:2505.00643 (2025).
6. Roy, Christopher W., et al. "Free-running cardiac and respiratory motion-resolved 5D whole-heart coronary cardiovascular magnetic resonance angiography in pediatric cardiac patients using ferumoxytol." Journal of Cardiovascular Magnetic Resonance 24.1 (2022): 39.
7. Ho, Jonathan, Ajay Jain, and Pieter Abbeel. "Denoising diffusion probabilistic models." Advances in neural information processing systems 33 (2020): 6840-6851.
8. Webber, George, and Andrew J. Reader. "Diffusion models for medical image reconstruction." BJR| Artificial Intelligence 1.1 (2024): ubae013.
9. Shi, Zhenning, et al. "Resfusion: Denoising diffusion probabilistic models for image restoration based on prior residual noise." Advances in Neural Information Processing Systems 37 (2024): 130664-130693.
10. Chung, Hyungjin, and Jong Chul Ye. "Score-based diffusion models for accelerated MRI." Medical image analysis 80 (2022): 102479.
11. Güngör, Alper, et al. "Adaptive diffusion priors for accelerated MRI reconstruction." Medical image analysis 88 (2023): 102872.
12. Sandino, Christopher M., et al. "Accelerating cardiac cine MRI using a deep learning‐based ESPIRiT reconstruction." Magnetic Resonance in Medicine 85.1 (2021): 152-167.
13. Yang, Junwei, et al. "End-to-end deep learning of non-rigid groupwise registration and reconstruction of dynamic MRI." Frontiers in cardiovascular medicine 9 (2022): 880186.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/508-02-003