Unified Diffusion model for Multi-contrast Ensembling Synthesis

Motivation: Scanning for multi-contrast MR images is time-consuming. To reduce scan time, it is beneficial to explore methods for efficiently synthesizing target contrast images from existing contrast scans. Goal(s): To address the stability issues encountered when dealing with multi-contrast MR image domains individually, we propose a methodology for effectively synthesizing images while incorporating multi-contrast domains. Approach: Our model is a novel unified diffusion model (UDM) that improves the synthesis of detailed anatomical structures in target contrast images through an ensemble method. Results: UDM demonstrates effectiveness across multiple domains, outperforming existing methodologies in synthesizing images for each contrast domain. Impact: By reducing scan times and costs for multi-contrast imaging, UDM facilitates prognosis prediction and treatment planning. This method is not only usable for image synthesis but also extendable to various applications such as reconstruction.