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Alessandro Piccolo

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Open access Sep 2026

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.

Alessandro Piccolo, V. Papapicco, A. Mariani et al. · 0 citations

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