Comparative study of the quantitative accuracy of oncological PET imaging based on deep learning methods.

ABSTRACT:

Background

[¹⁸F] Fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is an important tool for tumor assessment. Shortening scanning time and reducing the amount of radioactive tracer remain the most difficult challenges. Deep learning methods have provided powerful solutions, thus making it important to choose an appropriate neural network architecture.

Methods

A total of 311 tumor patients who underwent ¹⁸F-FDG PET/CT were retrospectively collected. The PET collection time was 3 min/bed. The first 15 and 30 s of each bed collection time were selected to simulate low-dose collection, and the pre-90s was used as the clinical standard protocol. Low-dose PET was used as input, convolutional neural network (CNN, 3D Unet as rep

SUBMITTER: Hu Y 

PROVIDER: S-EPMC10240003 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature