Deep learning-based low count whole-body positron emission tomography denoising incorporating computed tomography priors.

ABSTRACT:

Background

Deep-learning-based denoising improves image quality and quantification accuracy for low count (LC) positron emission tomography (PET). Conventional deep-learning-based denoising methods only require single LC PET image input. This study aims to propose a deep-learning-based LC PET denoising method incorporating computed tomography (CT) priors to further reduce the dose level.

Methods

Fifty patients who underwent their routine whole-body 2-deoxy-2-[¹⁸F]fluoro-D-glucose (¹⁸F-FDG) PET/CT scans in March 2022 were retrospectively and non-consecutively recruited. For full count (FC) PET, patients were injected with 3.7 MBq/kg FDG and scanned for 5 bed positions with 2 min/bed. LC PET of 1/10 (LC-10) and 1/20 (LC-20) count levels of FC PET were obt

SUBMITTER: Peng Z 

PROVIDER: S-EPMC11652058 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature