Plasma-CycleGAN: Plasma Biomarker-Guided MRI to PET Cross-modality Translation Using Conditional CycleGAN
Journal:
arXiv
Published Date:
Jan 4, 2025
Abstract
Cross-modality translation between MRI and PET imaging is challenging due to
the distinct mechanisms underlying these modalities. Blood-based biomarkers
(BBBMs) are revolutionizing Alzheimer's disease (AD) detection by identifying
patients and quantifying brain amyloid levels. However, the potential of BBBMs
to enhance PET image synthesis remains unexplored. In this paper, we performed
a thorough study on the effect of incorporating BBBM into deep generative
models. By evaluating three widely used cross-modality translation models, we
found that BBBMs integration consistently enhances the generative quality
across all models. By visual inspection of the generated results, we observed
that PET images generated by CycleGAN exhibit the best visual fidelity. Based
on these findings, we propose Plasma-CycleGAN, a novel generative model based
on CycleGAN, to synthesize PET images from MRI using BBBMs as conditions. This
is the first approach to integrate BBBMs in conditional cross-modality
translation between MRI and PET.
