Diffusion Modeling with Domain-conditioned Prior Guidance for
  Accelerated MRI and qMRI Reconstruction
Wanyu Bian Albert Jang Fang Liu
This study introduces a novel approach for image reconstruction based on a
diffusion model conditioned on the native data domain. Our method is applied to
multi-coil MRI and quantitative MRI reconstruction, leveraging the
domain-conditioned diffusion model within the frequency and parameter domains.
The prior MRI physics are used as embeddings in the diffusion model, enforcing
data consistency to guide the training and sampling process, characterizing MRI
k-space encoding in MRI reconstruction, and leveraging MR signal modeling for
qMRI reconstruction. Furthermore, a gradient descent optimization is
incorporated into the diffusion steps, enhancing feature learning and improving
denoising. The proposed method demonstrates a significant promise, particularly
for reconstructing images at high acceleration factors. Notably, it maintains
great reconstruction accuracy and efficiency for static and quantitative MRI
reconstruction across diverse anatomical structures. Beyond its immediate
applications, this method provides potential generalization capability, making
it adaptable to inverse problems across various domains.