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Abstract #2788

Generative Diffusion Model for Dynamic MRI Reconstruction with Temproal State Space Representation

Zhenlin Zhang1 and Chen Qin2
1Computing & I-X, Imperial College London, London, United Kingdom, 2Electrical and Electronic Engineering & I-X, Imperial College London, London, United Kingdom

Synopsis

Keywords: AI Diffusion Models, AI/ML Image Reconstruction

Motivation: In accelerated dynamic MR imaging, exploiting the temporal redundancy in the acquired data can play a crucial role in enhancing the precision and quality of image reconstruction.

Goal(s): To develop a denoising diffusion probabilistic model that leverages the temporal redundancy of dynamic MRI to improve the performance of reconstruction.

Approach: In this work, we propose a Mamba-based temporal block which can be easily plugged into the backbone of a diffusion model for exploiting the spatiotemporal redundancy within dynamic MRI.

Results: Qualitative and quantitative results show that our methods significantly improve reconstruction performance in multiple acceleration factors.

Impact: Our work will enable faster and higher-quality dynamic CMR imaging for improving the MR imaging workflow and aiding in clinical diagnosis.

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Keywords