Self-Consistent Nested Diffusion Bridge for Accelerated MRI Reconstruction

• URL: http://arxiv.org/abs/2412.09998v2
• Date: Mon, 28 Apr 2025 02:56:06 GMT
• Title: Self-Consistent Nested Diffusion Bridge for Accelerated MRI Reconstruction
• Authors: Tao Song, Yicheng Wu, Minhao Hu, Xiangde Luo, Guoting Luo, Guotai Wang, Yi Guo, Feng Xu, Shaoting Zhang,
• Abstract summary: We focus on the underexplored task of magnitude-image-based MRI reconstruction.<n>Recent advancements in diffusion models, particularly denoising diffusion probabilistic models, have demonstrated strong capabilities in modeling image priors.<n>We propose a novel Self-Consistent Nested Diffusion Bridge (SC-NDB) framework that models accelerated MRI reconstruction.
• Score: 22.589087990596887
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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