qDiMo: Domain-conditioned Diffusion Modeling for Accelerated qMRI Reconstruction

Motivation: Quantitative MRI (qMRI) is time-consuming and requires substantial efforts for acceleration to cut down the acquisition time. Goal(s): This paper proposes a novel generative AI approach for image reconstruction based on diffusion modeling conditioned on the native data domain. Approach: Our method is applied to multi-coil quantitative MRI reconstruction, leveraging the domain-conditioned diffusion model within the tissue parameter domain. Results: The proposed method demonstrates a significant promise for reconstructing quantitative maps at high acceleration factors. Notably, it maintains excellent reconstruction accuracy and efficiency for MR parameter maps across diverse anatomical structures. Impact: This work demonstrates the feasibility of a new generative AI method for rapid qMRI. Beyond its immediate applications, this method provides potential generalization capability, making it adaptable to inverse problems across various domains.