In this section
@inproceedings{Dayarathna:2025:10.1109/WACV61041.2025.00075,
author = {Dayarathna, S and Islam, KT and Zhuang, B and Yang, G and Cai, J and Law, M and Chen, Z},
doi = {10.1109/WACV61041.2025.00075},
pages = {670--679},
title = {McCaD: Multi-Contrast MRI Conditioned, Adaptive Adversarial Diffusion Model for High-Fidelity MRI Synthesis},
url = {http://dx.doi.org/10.1109/WACV61041.2025.00075},
year = {2025}
}
TY - CPAPER
AB - Magnetic Resonance Imaging (MRI) is instrumental in clinical diagnosis, offering diverse contrasts that provide comprehensive diagnostic information. However, acquiring multiple MRI contrasts is often constrained by high costs, long scanning durations, and patient discomfort. Current synthesis methods, typically focused on single-image contrasts, fall short in capturing the collective nuances across various contrasts. Moreover, existing methods for multi-contrast MRI synthesis often fail to accurately map feature-level information across multiple imaging contrasts. We introduce McCaD (Multi-Contrast MRI Conditioned Adaptive Adversarial Diffusion), a novel framework leveraging an adversarial diffusion model conditioned on multiple contrasts for high-fidelity MRI synthesis. McCaD significantly enhances synthesis accuracy by employing a multi-scale, feature-guided mechanism, incorporating denoising and semantic encoders. An adaptive feature maximization strategy and a spatial feature-attentive loss have been introduced to capture more intrinsic features across multiple contrasts. This facilitates a precise and comprehensive feature-guided denoising process. Extensive experiments on tumor and healthy multi-contrast MRI datasets demonstrated that the McCaD outperforms state-of-the-art baselines quantitively and qualitatively. The code is available at https://github.com/sanuwanihewa/M.cCaD.
AU - Dayarathna,S
AU - Islam,KT
AU - Zhuang,B
AU - Yang,G
AU - Cai,J
AU - Law,M
AU - Chen,Z
DO - 10.1109/WACV61041.2025.00075
EP - 679
PY - 2025///
SP - 670
TI - McCaD: Multi-Contrast MRI Conditioned, Adaptive Adversarial Diffusion Model for High-Fidelity MRI Synthesis
UR - http://dx.doi.org/10.1109/WACV61041.2025.00075
ER -
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Jan Sedlacik
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