Keywords: AI Diffusion Models, Machine Learning/Artificial Intelligence, Diffusion models, machine learning, deep learning, translation
Motivation: In medical image translation, denoising diffusion models (DDM) learn a task-irrelevant denoising transformation that maps Gaussian-noise onto a target-modality image, while receiving a source-modality image as a static-input channel. This causes suboptimal source-modality guidance due to a compromise between denoising and source-to-target transformations.
Goal(s): Our goal was to devise a new diffusion-based method that learns a task-relevant source-to-target transformation to improve translation fidelity.
Approach: We introduced a novel self-consistent recursive diffusion bridge (SelfRDB) that performs a gradual mapping from source onto target images.
Results: Higher performance was obtained with SelfRDB over previous state-of-the-art in multi-contrast MRI and MRI-CT translation.
Impact: The enhanced image fidelity in multi-modal protocols achieved by SelfRDB can extend the scope of imaging-based assessments, while maintaining relatively low scan budgets and minimizing exposure to invasive agents or radiation, particularly benefiting at-risk pediatric and elderly populations.
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