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

arXiv: https://arxiv.org/abs/2501.02146 · HTML: https://mlsanity.com/paper/2501.02146/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 4 Jan 2025.

## Builds on (most cited first, 6 of 6)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Bidirectional Mapping Generative Adversarial Networks for Brain MR to PET Synthesis](https://mlsanity.com/paper/2008.03483/) (2020)
- [MRI to PET Cross-Modality Translation using Globally and Locally Aware GAN (GLA-GAN) for Multi-Modal Diagnosis of Alzheimer's Disease](https://mlsanity.com/paper/2108.02160/) (2021)
- [GANBERT: Generative Adversarial Networks with Bidirectional Encoder Representations from Transformers for MRI to PET synthesis](https://mlsanity.com/paper/2008.04393/) (2020)
- [PASTA: Pathology-Aware MRI to PET Cross-Modal Translation with Diffusion Models](https://mlsanity.com/paper/2405.16942/) (2024)
- [FREA-Unet: Frequency-aware U-net for Modality Transfer](https://mlsanity.com/paper/2012.15397/) (2020)

## Built on by (most cited first, 3 of 3)

- [TauGenNet: Plasma-Driven Tau PET Image Synthesis via Text-Guided 3D Diffusion Models](https://mlsanity.com/paper/2509.04269/) (2025)
- [Translating MRI to PET through Conditional Diffusion Models with Enhanced Pathology Awareness](https://mlsanity.com/paper/2603.18896/) (2026)
- [When Brains Disagree: Biological Ambiguity Underlies the Challenge of Amyloid PET Synthesis from Structural MRI](https://mlsanity.com/paper/2605.11867/) (2026)

## Abstract (arXiv)

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.