Normal twin PET: personalized generative modeling for confounder correction and anomaly detection in whole-body PET/CT.

ABSTRACT: Variable physiological [¹⁸F]FDG uptake patterns and a lack of labelled data make it challenging to automatically distinguish normal from pathological suspicious uptake in whole-body PET/CT imaging. We propose a deep learning method that generates patient-specific normal twin PET images to serve as personalized references for quantitative analysis and unsupervised detection of pathological anomalies. We developed an image-to-image generative model that synthesizes normal reference twin PET (ntPET) images from CT scans, patient demographics, and acquisition parameters. The model was trained on 2,538 pseudo-normal PET/CT studies, including stable lymphoma patients and manually disease-masked clinical scans. Model performance was evaluated on 177 test studies achieving 89.3% explain

SUBMITTER: Hinge C 

PROVIDER: S-EPMC12663462 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature