Deep progressive learning achieves whole-body low-dose ¹⁸F-FDG PET imaging.

ABSTRACT:

Objectives

To validate a total-body PET-guided deep progressive learning reconstruction method (DPR) for low-dose ¹⁸F-FDG PET imaging.

Methods

List-mode data from the retrospective study (n = 26) were rebinned into short-duration scans and reconstructed with DPR. The standard uptake value (SUV) and tumor-to-liver ratio (TLR) in lesions and coefficient of variation (COV) in the liver in the DPR images were compared to the reference (OSEM images with full-duration data). In the prospective study, another 41 patients were injected with 1/3 of the activity based on the retrospective results. The DPR images (DPR_1/3(p)) were generated and compared with the reference (OSEM images with extended acquisition time). The SUV and COV were evaluated in three selected organs: l

SUBMITTER: Wang T 

PROVIDER: S-EPMC9681960 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature