# Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)

arXiv: https://arxiv.org/abs/1707.09747 · HTML: https://mlsanity.com/paper/1707.09747/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2017.

## Built on by (most cited first, 18 of 18)

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- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection](https://mlsanity.com/paper/2304.07147/) (2023)
- [DC-cycleGAN: Bidirectional CT-to-MR Synthesis from Unpaired Data](https://mlsanity.com/paper/2211.01293/) (2022)
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- [Energy-Guided Diffusion Model for CBCT-to-CT Synthesis](https://mlsanity.com/paper/2308.03354/) (2023)
- [CNNs and GANs in MRI-based cross-modality medical image estimation](https://mlsanity.com/paper/2106.02198/) (2021)
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- [AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics](https://mlsanity.com/paper/2110.10332/) (2021)
- [3-D PET Image Generation with tumour masks using TGAN](https://mlsanity.com/paper/2111.01866/) (2021)
- [PET Synthesis via Self-supervised Adaptive Residual Estimation Generative Adversarial Network](https://mlsanity.com/paper/2310.15550/) (2023)
- [MITS-GAN: Safeguarding Medical Imaging from Tampering with Generative Adversarial Networks](https://mlsanity.com/paper/2401.09624/) (2024)
- [Multi-modal Evidential Fusion Network for Trustworthy PET/CT Tumor Segmentation](https://mlsanity.com/paper/2406.18327/) (2024)
- [A Survey of Deep Fake Detection for Trial Courts](https://mlsanity.com/paper/2205.15792/) (2022)
- [Ensemble of Weak Spectral Total Variation Learners: a PET-CT Case Study](https://mlsanity.com/paper/2507.08735/) (2025)
- [A Dual Path Framework with Hotspot Guided Fusion for Three Dimensional CT to PET Synthesis in Head and Neck Cancer](https://mlsanity.com/paper/2607.21800/) (2026)

## Abstract (arXiv)

Positron emission tomography (PET) image synthesis plays an important role, which can be used to boost the training data for computer aided diagnosis systems. However, existing image synthesis methods have problems in synthesizing the low resolution PET images. To address these limitations, we propose multi-channel generative adversarial networks (M-GAN) based PET image synthesis method. Different to the existing methods which rely on using low-level features, the proposed M-GAN is capable to represent the features in a high-level of semantic based on the adversarial learning concept. In addition, M-GAN enables to take the input from the annotation (label) to synthesize the high uptake regions e.g., tumors and from the computed tomography (CT) images to constrain the appearance consistency and output the synthetic PET images directly. Our results on 50 lung cancer PET-CT studies indicate that our method was much closer to the real PET images when compared with the existing methods.