Archives of Acoustics, 48, 4, pp. 549–558, 2023
10.24425/aoa.2023.146815

Algorithm for Computationally Efficient Imaging of Sound Speed in Conventional Ultrasound Sonography

Piotr KARWAT
Institute of Fundamental Technological Research, Polish Academy of Sciences
Poland

The speed of sound (SoS) in tissues reflects their mechanical properties and therefore can carry valuable diagnostic information. In conventional ultrasound sonography (US), however, this information is not readily available. Furthermore, since the actual SoS is unknown, image reconstruction is carried out using an average SoS value for soft tissues. The resulting local deviations from the actual SoS lead to aberrations in US images. Methods for SoS imaging in US therefore have the potential to enable the correction of aberrations in classical US. In addition, they could also become a new US modality. There are several approaches to SoS image reconstruction. They differ in terms of input data requirements, computational complexity, imaging quality, and the potential for signal analysis at the intermediate stages of processing. This article presents an algorithm with multi-stage processing and low computational complexity. The algorithm was verified through numerical simulations and phantom measurements. The obtained results show that it can correctly estimate SoS in layered media, which in most cases model the tissue structure well. With its computational complexity of O(n), the algorithm can be implemented in real-time ultrasound imaging systems with limited hardware performance, such as portable ultrasound devices.
Keywords: speed of sound; ultrasound imaging; computational complexity
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Copyright © 2024 The Author(s). This work is licensed under the Creative Commons Attribution 4.0 International CC BY 4.0.

References

André M., Wiskin J., Borup D., Johnson S., Ojeda-Fournier H., Olson L. (2012), Quantitative volumetric breast imaging with 3D inverse scatter computed tomography, [in:] 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 34: 1110–1113, doi: 10.1109/embc.2012.6346129.

Cacko D., Lewandowski M. (2022), Shear wave elastography implementation on a portable research ultrasound system: Initial results, Applied Sciences, 12(12): 6210, doi: 10.3390/app12126210.

Chang S.H., Park S.B., Cho G.H. (1993), Phase-error-free quadrature sampling technique in the ultrasonic B-scan imaging system and its application to the synthetic focusing system, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 40(3): 216–223, doi: 10.1109/58.216834.

Cobbold R.S.C. (2007), Foundations of Biomedical Ultrasound, Oxford University Press.

Feigin M., Freedman D., Anthony B.W. (2020), A deep learning framework for single-sided sound speed inversion in medical ultrasound, IEEE Transactions on Biomedical Engineering, 67(4): 1142–1151, doi: 10.1109/tbme.2019.2931195.

Foundation for Research on Information Technologies in Society (n.d.), Speed of Sound, https://itis.swiss/virtual-population/tissue-properties/database/acousticproperties/speed-of-sound/ (access: 12.10.2023).

Ghoshal G., Lavarello R.J., Kemmerer J.P., Miller R.J., Oelze M.L. (2012), Ex vivo study of quantitative ultrasound parameters in fatty rabbit livers, Ultrasound in Medicine and Biology, 38(12): 2238–2248, doi: 10.1016/j.ultrasmedbio.2012.08.010.

Jaeger M., Held G., Preisser S., Peeters S., Grünig M., Frenz M. (2014), Computed ultrasound tomography in echo mode (CUTE) of speed of sound for diagnosis and for aberration correction in pulse-echo sonography, [in:] Proceedings of SPIE 9040, Medical Imaging 2014: Ultrasonic Imaging and Tomography, 9040: 90400A, doi: 10.1117/12.2042993.

Jaeger M., Held G., Peeters S., Preisser S., Grünig M., Frenz M. (2015), Computed ultrasound tomography in echo mode for imaging speed of sound using pulse-echo sonography: Proof of principle, Ultrasound in Medicine and Biology, 41(1): 235–250, doi: 10.1016/j.ultrasmedbio.2014.05.019.

Jaeger M., Frenz M. (2015), Quantitative imaging of speed of sound in echo ultrasonography, [in:] IEEE International Ultrasound Symposium, https://www.youtube.com/watch?v=Ck75XbfLQtY (access: 12.10.2023).

Jensen J.A. (1996), Field: A program for simulating ultrasound systems, [in:] Medical & Biological Engineering & Computing, 34(1): 351–353.

Karwat P. (2019), Computationally efficient algorithm for sound speed imaging in pulse-echo ultrasound, Proceedings of Meetings on Acoustics, 38(1): 020005, doi: 10.1121/2.0001109.

Lyons R.G. (2004), Understanding Digital Signal Processing, 2nd ed., Prentice Hall.

Sanabria S.J., Ozkan E., Rominger M., Goksel O. (2018), Spatial domain reconstruction for imaging speed-of-sound with pulse-echo ultrasound: Simulation and in vivo study, Physics in Medicine and Biology, 63(21): 215015, doi: 10.1088/1361-6560/aae2fb.

Stähli P., Kuriakose M., Frenz M., Jaeger M. (2020), Improved forward model for quantitative pulse-echo speed-of-sound imaging, Ultrasonics, 108: 106168, doi: 10.1016/j.ultras.2020.106168.

Treeby B.E., Cox B.T. (2010), k-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields, Journal of Biomedical Optics, 15(2): 021314, doi: 10.1117/1.3360308.

Young J.R., Schoen S., Kumar V., Thomenius K., Samir A.E. (2022), SoundAI: Improved imaging with learned sound speed maps, [in:] 2022 IEEE International Ultrasonics Symposium, doi: 10.1109/IUS54386.2022.9958284.




DOI: 10.24425/aoa.2023.146815