Deep-learning-derived input function in dynamic [¹⁸F]FDG PET imaging of mice.

ABSTRACT: Dynamic positron emission tomography and kinetic modeling play a critical role in tracer development research using small animals. Kinetic modeling from dynamic PET imaging requires accurate knowledge of an input function, ideally determined through arterial blood sampling. Arterial cannulation in mice, however, requires complex, time-consuming and terminal surgery, meaning that longitudinal studies are impossible. The aim of the current work was to develop and evaluate a non-invasive, deep-learning-based prediction model (DLIF) that directly takes the PET data as input to predict a usable input function. We first trained and evaluated the DLIF model on 68 [¹⁸F]Fluorodeoxyglucose mouse scans with image-derived targets using cross validation. Subsequently, we evaluated the perfor

SUBMITTER: Kuttner S 

PROVIDER: S-EPMC11460089 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature