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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References
Ali, R., et al.: Aberration correction in diagnostic ultrasound: a review of the prior field and current directions. Z. Med. Phys. (2023). https://doi.org/10.1016/j.zemedi.2023.01.003
Ali, R., Herickhoff, C.D., Hyun, D., Dahl, J.J., Bottenus, N.: Extending retrospective encoding for robust recovery of the multistatic data set. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 67(5), 943–956 (2019)
Bradbury, J., et al.: JAX: composable transformations of Python+NumPy programs (2018). http://github.com/google/jax
Feigin, M., Freedman, D., Anthony, B.W.: A deep learning framework for single-sided sound speed inversion in medical ultrasound. IEEE Trans. Biomed. Eng. 67(4), 1142–1151 (2020)
Hewish, A.: The diffraction of radio waves in passing through a phase-changing ionosphere. Proc. R. Soc. Lond. Ser. A. Math. Phys. Sci. 209(1096), 81–96 (1951)
Hollman, K., Rigby, K., O’donnell, M.: Coherence factor of speckle from a multi-row probe. In: Proceedings 1999 IEEE Ultrasonics Symposium (Cat. No. 99CH37027), vol. 2, pp. 1257–1260. IEEE (1999)
Hyun, D., Li, Y.L., Steinberg, I., Jakovljevic, M., Klap, T., Dahl, J.J.: An open source GPU-based beamformer for real-time ultrasound imaging and applications. In: 2019 IEEE International Ultrasonics Symposium (IUS), pp. 20–23. IEEE (2019)
ITISFoundation: Tissue properties database v4–1 (2022). https://doi.org/10.13099/VIP21000-04-01
Jakovljevic, M., Hsieh, S., Ali, R., Chau Loo Kung, G., Hyun, D., Dahl, J.J.: Local speed of sound estimation in tissue using pulse-echo ultrasound: model-based approach. J. Acoust. Soc. Am. 144, 254–266 (2018)
Jaros, J., Rendell, A.P., Treeby, B.E.: Full-wave nonlinear ultrasound simulation on distributed clusters with applications in high-intensity focused ultrasound. Int. J. High Perform. Comput. Appl. 30(2), 137–155 (2016)
Mallart, R., Fink, M.: Adaptive focusing in scattering media through sound-speed inhomogeneities: the van Cittert Zernike approach and focusing criterion. J. Acoust. Soc. Am. 96(6), 3721–3732 (1994)
Ng, G.C., Freiburger, P.D., Walker, W.F., Trahey, G.E.: A speckle target adaptive imaging technique in the presence of distributed aberrations. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 44(1), 140–151 (1997)
Nock, L., Trahey, G.E., Smith, S.W.: Phase aberration correction in medical ultrasound using speckle brightness as a quality factor. J. Acoust. Soc. Am. 85(5), 1819–1833 (1989)
Sanabria, S.J., Brevett, T., Ali, R., Telichko, A., Dahl, J.: Direct speed of sound reconstruction from full-synthetic aperture data with dual regularization. In: 2022 IEEE International Ultrasonics Symposium (IUS), pp. 1–4 (2022)
Sanabria, S.J., Ozkan, E., Rominger, M., Goksel, O.: Spatial domain reconstruction for imaging speed-of-sound with pulse-echo ultrasound: simulation and in vivo study. Phys. Med. Biol. 63(21), 215015 (2018)
Simson, W.A., Paschali, M., Sideri-Lampretsa, V., Navab, N., Dahl, J.J.: Investigating pulse-echo sound speed estimation in breast ultrasound with deep learning. arXiv preprint: arXiv:2302.03064 (2023)
Stähli, P., Kuriakose, M., Frenz, M., Jaeger, M.: Improved forward model for quantitative pulse-echo speed-of-sound imaging. Ultrasonics 108, 106168 (2020)
Vishnevskiy, V., Sanabria, S.J., Goksel, O.: Image reconstruction via variational network for real-time hand-held sound-speed imaging. In: Knoll, F., Maier, A., Rueckert, D. (eds.) MLMIR 2018. LNCS, vol. 11074, pp. 120–128. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00129-2_14
Wild, A., Hobbs, R., Frenje, L.: Modelling complex media: an introduction to the phase-screen method. Phys. Earth Planet. Inter. 120(3), 219–225 (2000)
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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