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

• URL: http://arxiv.org/abs/2503.15555v1
• Date: Tue, 18 Mar 2025 20:19:28 GMT
• Title: Whole-Body Image-to-Image Translation for a Virtual Scanner in a Healthcare Digital Twin
• Authors: Valerio Guarrasi, Francesco Di Feola, Rebecca Restivo, Lorenzo Tronchin, Paolo Soda,
• Abstract summary: 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.<n>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.
• Score: 0.8714814768600079
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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