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Abstract #0933

A Self-Consistent Diffusion Schrodinger Bridge for Multi-Modal Medical Image Translation

Fuat Arslan1,2, Bilal Kabas1,2, Onat Dalmaz1,2, Muzaffer Ozbey1,2, and Tolga Cukur1,2,3
1Dept. of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey, 2National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey, 3Dept. of Neuroscience, Bilkent University, Ankara, Turkey

Synopsis

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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Keywords