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Abstract #0122

dDiMo: Domain-conditioned and Temporal-guided Diffusion Modeling for Accelerated Dynamic MRI

Liping Zhang1, Iris Yuwen Zhou1, and Fang Liu1
1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States

Synopsis

Keywords: AI Diffusion Models, AI/ML Image Reconstruction

Motivation: Diffusion probabilistic methods synthesize realistic images from Gaussian noise but can yield suboptimal performance in reconstructing accelerated MRI data with additional time-resolved dimensions, causing temporal misalignment.

Goal(s): This paper aims to develop a novel generative AI approach based on diffusion modeling that incorporates temporal features from time-resolved dimensions, like dynamic cardiac motion, to generate high-quality images.

Approach: Our method introduces a domain-conditioned, temporal-guided diffusion model that leverages spatiotemporal correlations and self-consistent k-t priors to guide the diffusion process in the native data domain.

Results: The proposed method shows significant promise for multi-coil cardiac MRI reconstruction at high acceleration factors.

Impact: This work demonstrates the feasibility of a novel generative AI method for rapid dynamic MRI by leveraging temporal information and self-consistent k-t priors. Beyond its immediate applications, the method shows potential for generalization, adapting to inverse problems across various domains.

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