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Speed of sound ultrasound transmission tomography image reconstruction based on Bézier curves

UST
refraction-corrected ray-tracing
Bézier path solver
Mailyn Perez-Liva, José Manuel Udías, Jorge Camacho, Elena Merčep, Xosé Luís Deán-Ben, Daniel Razansky, Joaquín L. Herraiz
Ultrasonics, Volume 103, 2020, 106097
https://doi.org/10.1016/j.ultras.2020.106097

Speed of Sound (SoS) maps from ultrasound tomography (UST) provide valuable quantitative information for soft tissue characterization and identification of lesions, making this technique interesting for breast cancer detection. However, due to the complexity of the processes that characterize the interaction of ultrasonic waves with matter, classic and fast tomographic algorithms such as back-projection are not suitable. Consequently, the image reconstruction process in UST is generally slow compared to other more conventional medical tomography modalities. With the aim of facilitating the translation of this technique into real clinical practice, several reconstruction algorithms are being proposed to make image reconstruction in UST to be a fast and accurate process. The geometrical acoustic approximation is often used to reconstruct SoS with less computational burden in comparison with full-wave inversion methods. In this work, we propose a simple formulation to perform on-the-flight reconstruction for UST using geometrical acoustics with refraction correction based on quadratic Bézier polynomials. Here we demonstrate that the trajectories created with these polynomials are an accurate approximation to reproduce the refracted acoustic paths connecting the emitter and receiver transducers. The method is faster than typical acquisition times in UST. Thus, it can be considered a step towards real-time reconstructions, which may contribute to its future clinical translation.

Keywords UST Refraction-corrected ray-tracing Bézier path solver

Acknowledgments

Financial support is acknowledged from the European Research Council under grant ERC-2015-CoG-682379 and German Research Foundation Grant RA1848/5-1. JLH and JMU acknowledges partial support from the Spanish Government (FPA2015-65035-P, RTC-2015-3772-1, RTI2018-095800-A-I00) and Comunidad de Madrid (S2013/MIT-3024 TOPUS-CM, B2017/BMD-3888 PRONTO-CM), European Region-al Funds.

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