Whole-Body Image-to-Image Translation for a Virtual Scanner in a Healthcare Digital Twin
Journal:
arXiv
Published Date:
Mar 18, 2025
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.
