Plasma-CycleGAN: Plasma Biomarker-Guided MRI to PET Cross-modality Translation Using Conditional CycleGAN

TL;DR

This paper introduces Plasma-CycleGAN, a novel model that uses blood-based biomarkers to improve MRI-to-PET image translation, demonstrating enhanced quality and fidelity in generated PET images for Alzheimer's disease research.

Contribution

It is the first to incorporate blood-based biomarkers into conditional CycleGAN for cross-modality MRI to PET translation, improving generative quality.

Findings

BBBMs improve translation quality across models

CycleGAN achieves high visual fidelity in generated PET images

Plasma-CycleGAN outperforms existing methods in PET synthesis

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.