Accelerated MRI Reconstruction with Fourier-Constrained Diffusion Schrodinger Bridges

Motivation: Diffusion probabilistic methods synthesize realistic images via a denoising transformation from Gaussian noise onto MRI data, but this normality assumption can yield suboptimal performance in accelerated MRI reconstruction tasks. Goal(s): Our goal was to devise a new diffusion-based method that generates high-quality images by capturing a task-relevant transformation for accelerated MRI. Approach: We introduced a novel reconstruction method based on a diffusion Schrodinger bridge (FDB) that learns to directly transform between undersampled and fully-sampled MRI data via a multi-step process. Results: Higher reconstruction performance was obtained with FDB over previous state-of-the-art at up-to 8-fold acceleration. Impact: The improvement in image quality and acquisition speed in accelerated MRI enabled through FDB may facilitate comprehensive MRI exams in many applications, particularly in assessments of pediatric and elderly individuals in need of fast exams due to limited motor control.