Piecewise Dynamic Diffusion Regularization for Reconstruction of Cardiac Cine MRI

• URL: http://arxiv.org/abs/2607.03299v1
• Date: Fri, 03 Jul 2026 13:09:54 GMT
• Title: Piecewise Dynamic Diffusion Regularization for Reconstruction of Cardiac Cine MRI
• Abstract summary: Real-time cardiac cine MRI enables visualization of the beating heart during breathing, but severe under-breathing and motion challenge.<n>We propose Piecewise Dynamic Regularization (PDDR), a reconstruction method that integrates prior within a variational reconstruction framework.<n>Experiments on retrospectively accelerated and prospective real-time MRI demonstrate that PDDR outperforms classical unsupervised, and diffusion-based methods.
• Score: 18.223325969857907
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-time cardiac cine MRI enables visualization of the beating heart during free breathing, but severe undersampling and motion make reconstruction highly challenging. A central challenge for reconstruction is incorporating powerful priors of cardiac anatomy while remaining computationally efficient. We propose Piecewise Dynamic Diffusion Regularization (PDDR), a reconstruction method that integrates a spatiotemporal diffusion model as a generative prior within a variational reconstruction framework for cine MRI. The model employs dedicated spatial layers to encode anatomical structure and temporal layers to capture cardiac motion learned from gated cine data. PDDR leverages the dynamic prior in a piecewise manner, enabling the efficient use of spatiotemporal diffusion models for processing of long real-time sequences. Experiments on retrospectively accelerated and prospective real-time cine MRI demonstrate that PDDR outperforms classical, unsupervised, and diffusion-based methods, delivering high-quality reconstructions with substantially reduced computation time compared to state-of-the-art baselines. These results highlight PDDR as a practical and scalable solution for free-breathing, real-time cardiac MRI. Code is available at https://github.com/MLI-lab/pddr.

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