Abstract
Ultrasound images are distorted by phase aberration arising from local sound speed variations in the tissue, which lead to inaccurate time delays in beamforming and loss of image focus. Whereas state-of-the-art correction approaches rely on simplified physical models (e.g. phase screens), we propose a novel physics-based framework called differentiable beamforming that can be used to rapidly solve a wide range of imaging problems. We demonstrate the generalizability of differentiable beamforming by optimizing the spatial sound speed distribution in a heterogeneous imaging domain to achieve ultrasound autofocusing using a variety of physical constraints based on phase shift minimization, speckle brightness, and coherence maximization. The proposed method corrects for the effects of phase aberration in both simulation and in-vivo cases by improving image focus while simultaneously providing quantitative speed-of-sound distributions for tissue diagnostics, with accuracy improvements with respect to previously published baselines. Finally, we provide a broader discussion of applications of differentiable beamforming in other ultrasound domains.
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Acknowledgements
This work was supported in part by the National Institute of Biomedical Imaging and Bioengineering under Grant K99-EB032230 and Grant R01-EB027100, as well as the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1656518.
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Simson, W., Zhuang, L., Sanabria, S.J., Antil, N., Dahl, J.J., Hyun, D. (2023). Differentiable Beamforming for Ultrasound Autofocusing. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14229. Springer, Cham. https://doi.org/10.1007/978-3-031-43999-5_41
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