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