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DOI 10.1016/j.ultras.2020.106097

Медицинская библиотека · Ultrasonics · 2020

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

Perez-Liva Mailyn, Udías José Manuel, Camacho Jorge, Merčep Elena, Deán-Ben Xosé Luís, Razansky Daniel, Herraiz Joaquín L

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Аннотация

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.

Для цитирования: Perez-Liva Mailyn, Udías José Manuel, Camacho Jorge, Merčep Elena, Deán-Ben Xosé Luís, Razansky Daniel, Herraiz Joaquín L. Speed of sound ultrasound transmission tomography image reconstruction based on Bézier curves.. Ultrasonics. 2020. DOI: 10.1016/j.ultras.2020.106097.

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