Deep learning-assisted PET imaging achieves fast scan/low-dose examination.

ABSTRACT:

Purpose

This study aimed to investigate the impact of a deep learning (DL)-based denoising method on the image quality and lesion detectability of ¹⁸F-FDG positron emission tomography (PET) images.

Methods

Fifty-two oncological patients undergoing an ¹⁸F-FDG PET/CT imaging with an acquisition of 180 s per bed position were retrospectively included. The list-mode data were rebinned into four datasets: 100% (reference), 75%, 50%, and 33.3% of the total counts, and then reconstructed by OSEM algorithm and post-processed with the DL and Gaussian filter (GS). The image quality was assessed using a 5-point Likert scale, and FDG-avid lesions were counted to measure lesion detectability. Standardized uptake values (SUVs) in livers and lesions, liver signal-to-n

SUBMITTER: Xing Y 

PROVIDER: S-EPMC8816983 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature