External phantom-based validation of a deep-learning network trained for upscaling of digital low count PET data.

ABSTRACT:

Background

A reduction of dose and/or acquisition duration of PET examinations is desirable in terms of radiation protection, patient comfort and throughput, but leads to decreased image quality due to poorer image statistics. Recently, different deep-learning based methods have been proposed to improve image quality of low-count PET images. For example, one such approach allows the generation of AI-enhanced PET images (AI-PET) based on ultra-low count PET/CT scans. The performance of this algorithm has so far only been clinically evaluated on patient data featuring limited scan statistics and unknown actual activity concentration. Therefore, this study investigates the performance of this deep-learning algorithm using PET measurements of a phantom resembling different lesion sizes

SUBMITTER: Braune A 

PROVIDER: S-EPMC12003253 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature