# Bidirectional Mapping Generative Adversarial Networks for Brain MR to PET Synthesis

arXiv: https://arxiv.org/abs/2008.03483 · HTML: https://mlsanity.com/paper/2008.03483/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2020.

## Built on by (most cited first, 30 of 38)

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- [Morphological feature visualization of Alzheimer's disease via Multidirectional Perception GAN](https://mlsanity.com/paper/2111.12886/) (2021)
- [DecGAN: Decoupling Generative Adversarial Network detecting abnormal neural circuits for Alzheimer's disease](https://mlsanity.com/paper/2110.05712/) (2021)
- [Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience](https://mlsanity.com/paper/2206.07081/) (2022)
- [CATD: Unified Representation Learning for EEG-to-fMRI Cross-Modal Generation](https://mlsanity.com/paper/2408.00777/) (2024)
- [3D Brain Reconstruction by Hierarchical Shape-Perception Network from a Single Incomplete Image](https://mlsanity.com/paper/2107.11010/) (2021)
- [PASTA: Pathology-Aware MRI to PET Cross-Modal Translation with Diffusion Models](https://mlsanity.com/paper/2405.16942/) (2024)
- [Adversarial Learning Based Structural Brain-network Generative Model for Analyzing Mild Cognitive Impairment](https://mlsanity.com/paper/2208.08896/) (2022)
- [Cross-Modality Neuroimage Synthesis: A Survey](https://mlsanity.com/paper/2202.06997/) (2022)
- [Brain Structure-Function Fusing Representation Learning using Adversarial Decomposed-VAE for Analyzing MCI](https://mlsanity.com/paper/2305.14404/) (2023)
- [Alzheimer's Disease Prediction via Brain Structural-Functional Deep Fusing Network](https://mlsanity.com/paper/2309.16206/) (2023)
- [Functional Imaging Constrained Diffusion for Brain PET Synthesis from Structural MRI](https://mlsanity.com/paper/2405.02504/) (2024)
- [BrainMRDiff: A Diffusion Model for Anatomically Consistent Brain MRI Synthesis](https://mlsanity.com/paper/2504.04532/) (2025)
- [A Prior Guided Adversarial Representation Learning and Hypergraph Perceptual Network for Predicting Abnormal Connections of Alzheimer's Disease](https://mlsanity.com/paper/2110.09302/) (2021)
- [Cross-Modal Transformer GAN: A Brain Structure-Function Deep Fusing Framework for Alzheimer's Disease](https://mlsanity.com/paper/2206.13393/) (2022)
- [Ambient-Pix2PixGAN for Translating Medical Images from Noisy Data](https://mlsanity.com/paper/2402.01186/) (2024)
- [Plasma-CycleGAN: Plasma Biomarker-Guided MRI to PET Cross-modality Translation Using Conditional CycleGAN](https://mlsanity.com/paper/2501.02146/) (2025)
- [High-Fidelity Functional Ultrasound Reconstruction via A Visual Auto-Regressive Framework](https://mlsanity.com/paper/2505.21530/) (2025)
- [Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET](https://mlsanity.com/paper/2406.12632/) (2024)
- [Digital Staining with Knowledge Distillation: A Unified Framework for Unpaired and Paired-But-Misaligned Data](https://mlsanity.com/paper/2504.09899/) (2025)
- [Variable Augmented Network for Invertible Modality Synthesis-Fusion](https://mlsanity.com/paper/2109.00670/) (2021)
- [Generative AI Enables Structural Brain Network Construction from fMRI via Symmetric Diffusion Learning](https://mlsanity.com/paper/2309.16205/) (2023)
- [Language-Enhanced Generative Modeling for Amyloid PET Synthesis from MRI and Blood Biomarkers](https://mlsanity.com/paper/2511.02206/) (2025)
- [RelA-Diffusion: Relativistic Adversarial Diffusion for Multi-Tracer PET Synthesis from Multi-Sequence MRI](https://mlsanity.com/paper/2602.21345/) (2026)
- [Translating MRI to PET through Conditional Diffusion Models with Enhanced Pathology Awareness](https://mlsanity.com/paper/2603.18896/) (2026)

## Abstract (arXiv)

Fusing multi-modality medical images, such as MR and PET, can provide various anatomical or functional information about human body. But PET data is always unavailable due to different reasons such as cost, radiation, or other limitations. In this paper, we propose a 3D end-to-end synthesis network, called Bidirectional Mapping Generative Adversarial Networks (BMGAN), where image contexts and latent vector are effectively used and jointly optimized for brain MR-to-PET synthesis. Concretely, a bidirectional mapping mechanism is designed to embed the semantic information of PET images into the high dimensional latent space. And the 3D DenseU-Net generator architecture and the extensive objective functions are further utilized to improve the visual quality of synthetic results. The most appealing part is that the proposed method can synthesize the perceptually realistic PET images while preserving the diverse brain structures of different subjects. Experimental results demonstrate that the performance of the proposed method outperforms other competitive cross-modality synthesis methods in terms of quantitative measures, qualitative displays, and classification evaluation.