A Self-supervised Diffusion Bridge for MRI Reconstruction
Journal:
arXiv
Published Date:
Jan 6, 2025
Abstract
Diffusion bridges (DBs) are a class of diffusion models that enable faster
sampling by interpolating between two paired image distributions. Training
traditional DBs for image reconstruction requires high-quality reference
images, which limits their applicability to settings where such references are
unavailable. We propose SelfDB as a novel self-supervised method for training
DBs directly on available noisy measurements without any high-quality reference
images. SelfDB formulates the diffusion process by further sub-sampling the
available measurements two additional times and training a neural network to
reverse the corresponding degradation process by using the available
measurements as the training targets. We validate SelfDB on compressed sensing
MRI, showing its superior performance compared to the denoising diffusion
models.
