The Effect of Deep Learning on Radiomic Imaging Features: A Phantom Study

Abstract

Motivation: To investigate the effect of a deep learning reconstruction algorithm on radiomic image features. Goal(s): To assess the effect of AIRTM Recon Deep Learning (ARDL), a commercial AI reconstruction algorithm, on radiomic features in a set of phantoms. Approach: A set of radiomic phantoms were constructed and used to acquire images with different numbers of signal averages and ARDL levels. Effects were evaluated through intraclass correlation coefficient (ICC) measures. Results: Radiomic features maintain excellent ICC values (>0.9) at a constant SNR with ARDL Low, but ICC values decrease with higher ARDL levelsImpact: This research highlights how deep learning image reconstruction can alter radiomic features and could help define a subset of stable features. The level of deep learning reconstruction applied is shown to have significant impact, even at constant SNR.