Abstract:

Recent studies have introduced quantitative ultrasound (QUS) to extract tissue acoustic properties from pulse-echo data, employing neural network models to extract pathological features in raw radio-frequency (RF) signals. Nonetheless, implementing QUS on widely deployed ultrasound equipment faces two significant challenges. Firstly, the TX beam pattern required for QUS may differ from the pattern optimized for B-mode imaging, requiring additional steps such as a firmware update. Secondly, the variation in the number of transducer elements and their corresponding region of interests (ROI) requires retraining for each transducer. This paper presents a QUS imaging technique based on multi-scanline transmission (MST) beamforming, commonly employed in conventional ultrasound equipment. Additionally, we propose a unique network architecture and training data augmentation techniques that leverage MST’s distinctive properties. Furthermore, we demonstrate the effectiveness of the proposed QUS approach in untrained environments, making it readily applicable for clinical use.

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