Keywords: Analysis/Processing, Radiomics
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 levels
Impact: 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.
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