The Impact of Artificial Intelligence CNN Based Denoising on FDG PET Radiomics.

ABSTRACT:

Background

With a constantly increasing number of diagnostic images performed each year, Artificial Intelligence (AI) denoising methods offer an opportunity to respond to the growing demand. However, it may affect information in the image in an unknown manner. This study quantifies the effect of AI-based denoising on FDG PET textural information in comparison to a convolution with a standard gaussian postfilter (EARL1).

Methods

The study was carried out on 113 patients who underwent a digital FDG PET/CT (VEREOS, Philips Healthcare). 101 FDG avid lesions were segmented semi-automatically by a nuclear medicine physician. VOIs in the liver and lung as reference organs were contoured. PET textural features were extracted with pyradiomics. Texture features from AI denoised and EA

SUBMITTER: Jaudet C 

PROVIDER: S-EPMC8421788 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature