# Whole-Body Image-to-Image Translation for a Virtual Scanner in a Healthcare Digital Twin

arXiv: https://arxiv.org/abs/2503.15555 · HTML: https://mlsanity.com/paper/2503.15555/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 18 Mar 2025.

## Builds on (most cited first, 10 of 10)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1802.01221/) (2018)
- [A Systematic Review of Intermediate Fusion in Multimodal Deep Learning for Biomedical Applications](https://mlsanity.com/paper/2408.02686/) (2024)
- [Multimodal Explainability via Latent Shift applied to COVID-19 stratification](https://mlsanity.com/paper/2212.14084/) (2022)
- [Multi-objective optimization determines when, which and how to fuse deep networks: an application to predict COVID-19 outcomes](https://mlsanity.com/paper/2204.03772/) (2022)
- [A Deep Learning Approach for Virtual Contrast Enhancement in Contrast Enhanced Spectral Mammography](https://mlsanity.com/paper/2308.00471/) (2023)
- [Multi-Dataset Multi-Task Learning for COVID-19 Prognosis](https://mlsanity.com/paper/2405.13771/) (2024)
- [A Deep Learning Approach for Overall Survival Prediction in Lung Cancer with Missing Values](https://mlsanity.com/paper/2307.11465/) (2023)
- [A graph neural network-based model with Out-of-Distribution Robustness for enhancing Antiretroviral Therapy Outcome Prediction for HIV-1](https://mlsanity.com/paper/2312.17506/) (2023)

## Built on by (most cited first, 5 of 5)

- [Beyond a Single Mode: GAN Ensembles for Diverse Medical Data Generation](https://mlsanity.com/paper/2503.24258/) (2025)
- [Cross Modality Image Translation In Medical Imaging Using Generative Frameworks](https://mlsanity.com/paper/2605.13686/) (2026)
- [Longitudinal NSCLC Treatment Progression via Multimodal Generative Models](https://mlsanity.com/paper/2603.06147/) (2026)
- [Virtual Scanning for NSCLC Histology: Investigating the Discriminatory Power of Synthetic PET](https://mlsanity.com/paper/2605.02746/) (2026)
- [3D CT-to-PET Translation via Latent Brownian Bridge Diffusion](https://mlsanity.com/paper/2609.12860/) (2026)

## Abstract (arXiv)

Generating positron emission tomography (PET) images from computed tomography (CT) scans via deep learning offers a promising pathway to reduce radiation exposure and costs associated with PET imaging, improving patient care and accessibility to functional imaging. Whole-body image translation presents challenges due to anatomical heterogeneity, often limiting generalized models. We propose a framework that segments whole-body CT images into four regions-head, trunk, arms, and legs-and uses district-specific Generative Adversarial Networks (GANs) for tailored CT-to-PET translation. Synthetic PET images from each region are stitched together to reconstruct the whole-body scan. Comparisons with a baseline non-segmented GAN and experiments with Pix2Pix and CycleGAN architectures tested paired and unpaired scenarios. Quantitative evaluations at district, whole-body, and lesion levels demonstrated significant improvements with our district-specific GANs. Pix2Pix yielded superior metrics, ensuring precise, high-quality image synthesis. By addressing anatomical heterogeneity, this approach achieves state-of-the-art results in whole-body CT-to-PET translation. This methodology supports healthcare Digital Twins by enabling accurate virtual PET scans from CT data, creating virtual imaging representations to monitor, predict, and optimize health outcomes.