Abstract
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.
Abstract (translated)
加速的磁共振成像（MRI）重建技术旨在减少检查时间同时保持高图像保真度，这对于临床环境中提高患者舒适度和医院效率非常理想。现有的深度学习方法通常从欠采样数据中进行传统重建方式来重构图像，但它们仍难以提供高质量的结果。近年来，扩散模型显示出显著提升生成图像保真度的潜力。然而，这些模型的推理过程从随机高斯噪声开始引入了结果不稳定，并且通常需要数千次采样步骤，导致重建质量不佳和效率低下。为了解决这些问题，我们提出了循环一致性桥接扩散模型（CBDM）。CBDM采用两个桥接扩散模型构建一个带有一致损失的循环一致性扩散过程，增强重建图像的细节并减少扩散步骤的数量。此外，CBDM集成了轮廓波分解嵌入模块（CDEM），通过频率域分解金字塔和方向滤波器组捕捉图像中的多尺度结构纹理知识，以提高结构保真度。广泛的实验表明，我们的模型在快MRI和IXI数据集中通过更高的重建质量和更少的训练迭代次数实现了加速MRI重建的新技术标杆。
URL
https://arxiv.org/abs/2412.09998
