# Virtual PET Images from CT Data Using Deep Convolutional Networks: Initial Results

arXiv: https://arxiv.org/abs/1707.09585 · HTML: https://mlsanity.com/paper/1707.09585/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2017.

## Built on by (most cited first, 9 of 9)

- [Conversion Between CT and MRI Images Using Diffusion and Score-Matching Models](https://mlsanity.com/paper/2209.12104/) (2022)
- [Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis](https://mlsanity.com/paper/2204.06929/) (2022)
- [Similarity and Quality Metrics for MR Image-To-Image Translation](https://mlsanity.com/paper/2405.08431/) (2024)
- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [Controllable cardiac synthesis via disentangled anatomy arithmetic](https://mlsanity.com/paper/2107.01748/) (2021)
- [Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection](https://mlsanity.com/paper/2304.07147/) (2023)
- [Generative adversarial network with object detector discriminator for enhanced defect detection on ultrasonic B-scans](https://mlsanity.com/paper/2106.04281/) (2021)
- [MITS-GAN: Safeguarding Medical Imaging from Tampering with Generative Adversarial Networks](https://mlsanity.com/paper/2401.09624/) (2024)
- [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)

In this work we present a novel system for PET estimation using CT scans. We explore the use of fully convolutional networks (FCN) and conditional generative adversarial networks (GAN) to export PET data from CT data. Our dataset includes 25 pairs of PET and CT scans where 17 were used for training and 8 for testing. The system was tested for detection of malignant tumors in the liver region. Initial results look promising showing high detection performance with a TPR of 92.3% and FPR of 0.25 per case. Future work entails expansion of the current system to the entire body using a much larger dataset. Such a system can be used for tumor detection and drug treatment evaluation in a CT-only environment instead of the expansive and radioactive PET-CT scan.