Accelerated MRI reconstruction techniques aim to reduce examination time while maintaining high image fidelity, which is highly desirable in clinical settings for improving patient comfort and hospital efficiency. Existing deep learning methods typically reconstruct images from under-sampled data with traditional reconstruction approaches, but they still struggle to provide high-fidelity results. Diffusion models show great potential to improve fidelity of generated images in recent years. However, their inference process starting with a random Gaussian noise introduces instability into the results and usually requires thousands of sampling steps, resulting in sub-optimal reconstruction quality and low efficiency. To address these challenges, we propose Cycle-Consistent Bridge Diffusion Model (CBDM). CBDM employs two bridge diffusion models to construct a cycle-consistent diffusion process with a consistency loss, enhancing the fine-grained details of reconstructed images and reducing the number of diffusion steps. Moreover, CBDM incorporates a Contourlet Decomposition Embedding Module (CDEM) which captures multi-scale structural texture knowledge in images through frequency domain decomposition pyramids and directional filter banks to improve structural fidelity. Extensive experiments demonstrate the superiority of our model by higher reconstruction quality and fewer training iterations, achieving a new state of the art for accelerated MRI reconstruction in both fastMRI and IXI datasets.

翻译：加速磁共振成像重建技术旨在缩短检查时间的同时保持高图像保真度，这对于提升患者舒适度和医院效率的临床环境具有极高价值。现有深度学习方法通常基于传统重建方法从欠采样数据中重建图像，但仍难以提供高保真度结果。近年来，扩散模型在提升生成图像保真度方面展现出巨大潜力。然而，其从随机高斯噪声开始的推断过程会导致结果不稳定，且通常需要数千次采样步骤，导致重建质量欠佳且效率低下。为应对这些挑战，我们提出循环一致性桥接扩散模型。该模型采用两个桥接扩散模型构建具有一致性损失的循环一致性扩散过程，以增强重建图像的细粒度细节并减少扩散步骤数。此外，模型引入轮廓波分解嵌入模块，通过频域分解金字塔和方向滤波器组捕获图像中的多尺度结构纹理知识，从而提升结构保真度。大量实验表明，我们的模型凭借更高的重建质量和更少的训练迭代次数展现出优越性，在fastMRI和IXI数据集上实现了加速磁共振成像重建的最新性能。