Unified Diffusion model for Multi-contrast Ensembling Synthesis

Yeeun Lee, Yejee Shin, Doohyun Park, Geonhui Son, Taejoon Eo, Dosik Hwang

Abstract:

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 synthesizingtarget 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 foreffectively 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 imagesthrough an ensemble method.
Results:
UDM demonstrates effectiveness across multiple domains, outperforming existing methodologies in synthesizing images for each contrastdomain.
Impact:
By reducing scan times and costs for multi-contrast imaging, UDM facilitates prognosis prediction and treatment planning. This method is notonly usable for image synthesis but also extendable to various applications such as reconstruction.