A fully automated, expert-perceptive image quality assessment system for whole-body [18F]FDG PET/CT.

ABSTRACT:

Background

The quality of clinical PET/CT images is critical for both accurate diagnosis and image-based research. However, current image quality assessment (IQA) methods predominantly rely on handcrafted features and region-specific analyses, thereby limiting automation in whole-body and multicenter evaluations. This study aims to develop an expert-perceptive deep learning-based IQA system for [18F]FDG PET/CT to tackle the lack of automated, interpretable assessments of clinical whole-body PET/CT image quality.

Methods

This retrospective multicenter study included clinical whole-body [18F]FDG PET/CT scans from 718 patients. Automated identification and localization algorithms were applied to select predefined pairs of PET and CT slices from whole-body images. Fifteen experi

SUBMITTER: Zhang C 

PROVIDER: S-EPMC12008089 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature