Deep learning reconstruction improves radiomics feature stability and discriminative power in abdominal CT imaging: a phantom study.

ABSTRACT:

Objectives

To compare image quality of deep learning reconstruction (AiCE) for radiomics feature extraction with filtered back projection (FBP), hybrid iterative reconstruction (AIDR 3D), and model-based iterative reconstruction (FIRST).

Methods

Effects of image reconstruction on radiomics features were investigated using a phantom that realistically mimicked a 65-year-old patient's abdomen with hepatic metastases. The phantom was scanned at 18 doses from 0.2 to 4 mGy, with 20 repeated scans per dose. Images were reconstructed with FBP, AIDR 3D, FIRST, and AiCE. Ninety-three radiomics features were extracted from 24 regions of interest, which were evenly distributed across three tissue classes: normal liver, metastatic core, and metastatic rim. Features were analyzed in term

SUBMITTER: Michallek F 

PROVIDER: S-EPMC9213380 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature