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This study introduces a novel approach for image reconstruction based on adiffusion model conditioned on the native data domain. Our method is applied tomulti-coil MRI and quantitative MRI reconstruction, leveraging thedomain-conditioned diffusion model within the frequency and parameter domains.The prior MRI physics are used as embeddings in the diffusion model, enforcingdata consistency to guide the training and sampling process, characterizing MRIk-space encoding in MRI reconstruction, and leveraging MR signal modeling forqMRI reconstruction. Furthermore, a gradient descent optimization isincorporated into the diffusion steps, enhancing feature learning and improvingdenoising. The proposed method demonstrates a significant promise, particularlyfor reconstructing images at high acceleration factors. Notably, it maintainsgreat reconstruction accuracy and efficiency for static and quantitative MRIreconstruction across diverse anatomical structures. Beyond its immediateapplications, this method provides potential generalization capability, makingit adaptable to inverse problems across various domains.

Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction