V. Papapicco

A radiomics-based machine learning framework for full-reference ultrasound image quality assessment across varying acquisition settings.

A radiomics-based machine-learning framework for FR-IQA under controlled acquisition-setting variations provides an interpretable approach for characterizing acquisition-dependent ultrasound image quality, with potential utility for system evaluation and protocol optimization.