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arXiv · 2503.15555

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

Valerio Guarrasi·Francesco Di Feola·Rebecca Restivo·Lorenzo Tronchin·Paolo Soda
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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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Valerio Guarrasi, Francesco Di Feola, Rebecca Restivo, Lorenzo Tronchin, Paolo Soda. 2025-03-18. Whole-Body Image-to-Image Translation for a Virtual Scanner in a Healthcare Digital Twin. https://doi.org/10.1109/cbms65348.2025.00114

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