Self-Consistent Nested Diffusion Bridge for Accelerated MRI Reconstruction
Journal:
arXiv
Published Date:
Dec 13, 2024
Abstract
Accelerated MRI reconstruction plays a vital role in reducing scan time while
preserving image quality. While most existing methods rely on complex-valued
image-space or k-space data, these formats are often inaccessible in clinical
practice due to proprietary reconstruction pipelines, leaving only magnitude
images stored in DICOM files. To address this gap, we focus on the
underexplored task of magnitude-image-based MRI reconstruction. Recent
advancements in diffusion models, particularly denoising diffusion
probabilistic models (DDPMs), have demonstrated strong capabilities in modeling
image priors. However, their task-agnostic denoising nature limits performance
in source-to-target image translation tasks, such as MRI reconstruction. In
this work, we propose a novel Self-Consistent Nested Diffusion Bridge (SC-NDB)
framework that models accelerated MRI reconstruction as a bi-directional image
translation process between under-sampled and fully-sampled magnitude MRI
images. SC-NDB introduces a nested diffusion architecture with a
self-consistency constraint and reverse bridge diffusion pathways to improve
intermediate prediction fidelity and better capture the explicit priors of
source images. Furthermore, we incorporate a Contour Decomposition Embedding
Module (CDEM) to inject structural and textural knowledge by leveraging
Laplacian pyramids and directional filter banks. Extensive experiments on the
fastMRI and IXI datasets demonstrate that our method achieves state-of-the-art
performance compared to both magnitude-based and non-magnitude-based diffusion
models, confirming the effectiveness and clinical relevance of SC-NDB.
