Phase-Adaptive Averaging and Score-Based Denoising for Inverse Problems in Diffusion Imaging

Diffusion magnetic resonance imaging (dMRI) is widely used to map structural connectivity in the human brain, requiring the acquisition of ~100 diffusion-weighted images across multiple diffusion strengths (b-values) and gradient directions. Lower baseline SNR and higher acceleration factors remain a challenge for higher resolution dMRI, necessitating artificial intelligence (AI) based computational imaging methods to solve an inverse problem. However, existing AI methods yield limited gains in dMRI due to large SNR variations across b-values and phase inconsistencies introduced by diffusion gradients. In this work, we address these challenges with a novel loss-augmentation strategy for training physics-driven AI reconstructions. Our method boosts the SNR of high-b-value images through a combination of averaging and denoising. Phase adaptation is used in the former to handle phase mismatches among images, while a score-based approach is used for the latter. Experiments comparing our method with conventional reconstructions and standard AI methods demonstrate improved reconstruction quality.