Self-Consistent Nested Diffusion Bridge for Accelerated MRI Reconstruction

TL;DR

This paper introduces a novel diffusion-based framework, SC-NDB, for accelerated MRI reconstruction from magnitude images, employing a nested architecture, self-consistency, and structural embeddings to outperform existing methods.

Contribution

The paper presents a self-consistent nested diffusion bridge with structural embedding for improved magnitude-image MRI reconstruction, addressing the gap in clinical data formats.

Findings

Achieves state-of-the-art results on fastMRI and IXI datasets.

Outperforms existing diffusion models in MRI reconstruction.

Demonstrates clinical relevance and robustness of the proposed method.

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.