Keywords: Acquisition Methods, Brain
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.
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