# Cross-Modality Synthesis from CT to PET using FCN and GAN Networks for Improved Automated Lesion Detection

arXiv: https://arxiv.org/abs/1802.07846 · HTML: https://mlsanity.com/paper/1802.07846/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2018.

## Built on by (most cited first, 29 of 29)

- [ResViT: Residual vision transformers for multi-modal medical image synthesis](https://mlsanity.com/paper/2106.16031/) (2021)
- [Conversion Between CT and MRI Images Using Diffusion and Score-Matching Models](https://mlsanity.com/paper/2209.12104/) (2022)
- [Knowledge distillation from multi-modal to mono-modal segmentation networks](https://mlsanity.com/paper/2106.09564/) (2021)
- [Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis](https://mlsanity.com/paper/2307.10094/) (2023)
- [Has Multimodal Learning Delivered Universal Intelligence in Healthcare? A Comprehensive Survey](https://mlsanity.com/paper/2408.12880/) (2024)
- [Free-form tumor synthesis in computed tomography images via richer generative adversarial network](https://mlsanity.com/paper/2104.09701/) (2021)
- [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)
- [Multi-scale Transformer Network with Edge-aware Pre-training for Cross-Modality MR Image Synthesis](https://mlsanity.com/paper/2212.01108/) (2022)
- [Enhanced Multimodal Representation Learning with Cross-modal KD](https://mlsanity.com/paper/2306.07646/) (2023)
- [Synthetic Data in Radiological Imaging: Current State and Future Outlook](https://mlsanity.com/paper/2407.01561/) (2024)
- [HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis](https://mlsanity.com/paper/2203.05564/) (2022)
- [Pseudo-Data based Self-Supervised Federated Learning for Classification of Histopathological Images](https://mlsanity.com/paper/2205.15530/) (2022)
- [CT to PET Translation: A Large-scale Dataset and Domain-Knowledge-Guided Diffusion Approach](https://mlsanity.com/paper/2410.21932/) (2024)
- [ASL to PET Translation by a Semi-supervised Residual-based Attention-guided Convolutional Neural Network](https://mlsanity.com/paper/2103.05116/) (2021)
- [CNNs and GANs in MRI-based cross-modality medical image estimation](https://mlsanity.com/paper/2106.02198/) (2021)
- [Generative adversarial network with object detector discriminator for enhanced defect detection on ultrasonic B-scans](https://mlsanity.com/paper/2106.04281/) (2021)
- [AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics](https://mlsanity.com/paper/2110.10332/) (2021)
- [Brain MRI-to-PET Synthesis using 3D Convolutional Attention Networks](https://mlsanity.com/paper/2211.12082/) (2022)
- [Multi-task Deep Learning for Cerebrovascular Disease Classification and MRI-to-PET Translation](https://mlsanity.com/paper/2202.06142/) (2022)
- [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)
- [Tumor Synthesis conditioned on Radiomics](https://mlsanity.com/paper/2509.24182/) (2025)
- [Complementary Information Mutual Learning for Multimodality Medical Image Segmentation](https://mlsanity.com/paper/2401.02717/) (2024)
- [Predicting Hypoxia in Brain Tumors from Multiparametric MRI](https://mlsanity.com/paper/2401.14171/) (2024)
- [Machine Learning Techniques for MRI Data Processing at Expanding Scale](https://mlsanity.com/paper/2404.14326/) (2024)
- [Ensemble of Weak Spectral Total Variation Learners: a PET-CT Case Study](https://mlsanity.com/paper/2507.08735/) (2025)
- [Do We Really Need Diffusion? A Fast U-Net for Paired Medical Image Translation](https://mlsanity.com/paper/2606.17675/) (2026)
- [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)
- [3D CT-to-PET Translation via Latent Brownian Bridge Diffusion](https://mlsanity.com/paper/2609.12860/) (2026)

## Abstract (arXiv)

In this work we present a novel system for generation of virtual PET images using CT scans. We combine a fully convolutional network (FCN) with a conditional generative adversarial network (GAN) to generate simulated PET data from given input CT data. The synthesized PET can be used for false-positive reduction in lesion detection solutions. Clinically, such solutions may enable lesion detection and drug treatment evaluation in a CT-only environment, thus reducing the need for the more expensive and radioactive PET/CT scan. Our dataset includes 60 PET/CT scans from Sheba Medical center. We used 23 scans for training and 37 for testing. Different schemes to achieve the synthesized output were qualitatively compared. Quantitative evaluation was conducted using an existing lesion detection software, combining the synthesized PET as a false positive reduction layer for the detection of malignant lesions in the liver. Current results look promising showing a 28% reduction in the average false positive per case from 2.9 to 2.1. The suggested solution is comprehensive and can be expanded to additional body organs, and different modalities.