Deep learning-assisted ultra-fast/low-dose whole-body PET/CT imaging.

ABSTRACT:

Purpose

Tendency is to moderate the injected activity and/or reduce acquisition time in PET examinations to minimize potential radiation hazards and increase patient comfort. This work aims to assess the performance of regular full-dose (FD) synthesis from fast/low-dose (LD) whole-body (WB) PET images using deep learning techniques.

Methods

Instead of using synthetic LD scans, two separate clinical WB ¹⁸F-Fluorodeoxyglucose (¹⁸F-FDG) PET/CT studies of 100 patients were acquired: one regular FD (~ 27 min) and one fast or LD (~ 3 min) consisting of 1/8^th of the standard acquisition time. A modified cycle-consistent generative adversarial network (CycleGAN) and residual neural network (ResNET) models, denoted as CGAN and RNET, respectively, were

SUBMITTER: Sanaat A 

PROVIDER: S-EPMC8241799 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature