Keywords: Image Reconstruction, Machine Learning/Artificial Intelligence, deep learning reconstructionDeep learning (DL)-based techniques are increasingly being applied to assist with or improve image reconstruction. However, the impact of DL-based algorithms on radiomics is not well understood. This study aims to evaluate the impact of two commercially available DL-based reconstruction pipelines: (1) SmartSpeed (Philips Healthcare, U.S. FDA-cleared); and (2) SmartSpeed with Super Resolution (SmartSpeed+SuperRes, not U.S. FDA-cleared to date) on MRI radiomic features. Our analysis showed that compared to conventional image reconstruction technique, 42 out of 86 investigated radiomic features from SmartSpeed images were highly correlated whereas only 13 features from SmartSpeed+SuperRes images had high correlations with conventional image features.
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