Self-Consistent Recursive Diffusion Bridge for Medical Image Translation

• URL: http://arxiv.org/abs/2405.06789v1
• Date: Fri, 10 May 2024 19:39:55 GMT
• Title: Self-Consistent Recursive Diffusion Bridge for Medical Image Translation
• Authors: Fuat Arslan, Bilal Kabas, Onat Dalmaz, Muzaffer Ozbey, Tolga Çukur,
• Abstract summary: Denoising diffusion models (DDMs) have gained recent traction in medical image translation given improved training stability over adversarial models. We propose a novel self-consistent iterative diffusion bridge (SelfRDB) for improved performance in medical image translation. Comprehensive analyses in multi-contrast MRI and MRI-CT translation indicate that SelfRDB offers superior performance against competing methods.
• Score: 6.850683267295248
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Denoising diffusion models (DDM) have gained recent traction in medical image translation given improved training stability over adversarial models. DDMs learn a multi-step denoising transformation to progressively map random Gaussian-noise images onto target-modality images, while receiving stationary guidance from source-modality images. As this denoising transformation diverges significantly from the task-relevant source-to-target transformation, DDMs can suffer from weak source-modality guidance. Here, we propose a novel self-consistent recursive diffusion bridge (SelfRDB) for improved performance in medical image translation. Unlike DDMs, SelfRDB employs a novel forward process with start- and end-points defined based on target and source images, respectively. Intermediate image samples across the process are expressed via a normal distribution with mean taken as a convex combination of start-end points, and variance from additive noise. Unlike regular diffusion bridges that prescribe zero variance at start-end points and high variance at mid-point of the process, we propose a novel noise scheduling with monotonically increasing variance towards the end-point in order to boost generalization performance and facilitate information transfer between the two modalities. 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 analyses in multi-contrast MRI and MRI-CT translation indicate that SelfRDB offers superior performance against competing methods.

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