Arslan Fuat, Kabas Bilal, Dalmaz Onat, Ozbey Muzaffer, Çukur Tolga
Department of Electrical and Electronics Engineering, Bilkent University, Ankara 06800, Turkey; National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara 06800, Turkey.
Department of Electrical and Electronics Engineering, Bilkent University, Ankara 06800, Turkey; National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara 06800, Turkey; Neuroscience Program, Bilkent University, Ankara 06800, Turkey.
Med Image Anal. 2025 Dec;106:103747. doi: 10.1016/j.media.2025.103747. Epub 2025 Aug 5.
Denoising diffusion models (DDM) have gained recent traction in medical image translation given their high training stability and image fidelity. DDMs learn a multi-step denoising transformation that progressively maps random Gaussian-noise images provided as input onto target-modality images as output, while receiving indirect guidance from source-modality images via a separate static channel. This denoising transformation diverges significantly from the task-relevant source-to-target modality transformation, as source images are governed by a non-noise distribution. In turn, DDMs can suffer from suboptimal source-modality guidance and performance losses in medical image translation. Here, we propose a novel self-consistent recursive diffusion bridge (SelfRDB) that leverages direct source-modality guidance within its diffusion process for improved performance in medical image translation. Unlike DDMs, SelfRDB devises a novel forward process with the start-point taken as the target image, and the end-point defined based on the source image. Intermediate image samples across the process are expressed via a normal distribution whose mean is taken as a convex combination of start-end points, and whose variance is controlled by additive noise. Unlike regular diffusion bridges that prescribe zero noise variance at start-end points and high noise variance at mid-point of the process, we propose a novel noise scheduling with monotonically increasing variance towards the end-point in order to facilitate information transfer between the two modalities and boost robustness against measurement noise. To further enhance sampling accuracy in each reverse step, we propose a novel sampling procedure where the network recursively generates a transient-estimate of the target image until convergence onto a self-consistent solution. Comprehensive experiments in multi-contrast MRI and MRI-CT translation indicate that SelfRDB achieves state-of-the-art results in terms of image quality.
去噪扩散模型（DDM）因其高训练稳定性和图像保真度，近年来在医学图像翻译领域受到关注。DDM学习一种多步去噪变换，该变换将作为输入提供的随机高斯噪声图像逐步映射为目标模态图像作为输出，同时通过一个单独的静态通道从源模态图像接收间接引导。这种去噪变换与任务相关的源到目标模态变换有很大差异，因为源图像受非噪声分布支配。反过来，DDM在医学图像翻译中可能会受到次优的源模态引导和性能损失。在此，我们提出一种新颖的自一致递归扩散桥（SelfRDB），它在其扩散过程中利用直接的源模态引导来提高医学图像翻译的性能。与DDM不同，SelfRDB设计了一种新颖的正向过程，起点为目标图像，终点基于源图像定义。整个过程中的中间图像样本通过正态分布表示，其均值作为起点和终点的凸组合，方差由加性噪声控制。与常规扩散桥在起点和终点规定零噪声方差、在过程中点规定高噪声方差不同，我们提出一种新颖的噪声调度，方差朝着终点单调增加，以促进两种模态之间的信息传递，并增强对测量噪声的鲁棒性。为了在每个反向步骤中进一步提高采样精度，我们提出一种新颖的采样过程，网络在该过程中递归生成目标图像的瞬态估计，直到收敛到自一致解。在多对比度MRI和MRI-CT翻译中的综合实验表明，SelfRDB在图像质量方面取得了最优结果。
