Lens-Aware Differentiable Beamforming for In Vivo Distributed Aberration Correction with Curvilinear Transducers

Abstract

This work extends ultrasound autofocusing via common midpoint phase error optimization to support curvilinear array geometries. Iterative model-based aberration correction via local sound speed estimation is performed by accounting for refraction caused by the transducer lens by using a differentiable bent-ray tracing approach. Model validation is performed in silico, using calibrated sound speed phantoms, and on in vivo human liver images. This work represents the first large-scale in vivo validation of our distributed aberration-correction method on 321 acquisitions from 81 high-BMI human liver subjects. In acquired images with anechoic regions, average contrast and CNR improved by $1.42\pm 1.63$ dB (+18.4%), and $0.09\pm 0.14$ (+10.3%), respectively. An average improvement was also observed across multiple image quality metrics: speckle brightness (+20.1%), coherence factor (+13.1%), lag-one coherence (+2.6%), common-midpoint correlation coefficient (+0.7%), and common-midpoint phase error (-9.1%, lower is better). All metric improvements were statistically significant. Additionally, a significant qualitative improvement in target structure and visibility was observed. These results demonstrate the potential for future clinical distributed aberration correction techniques using ultrasound autofocusing.