Citas bibligráficas
Merino, M., (2024). Development and implementation of a shear wave speed estimation algorithm for crawling waves sonoelastography [Pontificia Universidad Católica del Perú]. http://hdl.handle.net/20.500.12404/29148
Merino, M., Development and implementation of a shear wave speed estimation algorithm for crawling waves sonoelastography []. PE: Pontificia Universidad Católica del Perú; 2024. http://hdl.handle.net/20.500.12404/29148
@misc{renati/1659921,
title = "Development and implementation of a shear wave speed estimation algorithm for crawling waves sonoelastography",
author = "Merino Acuña, Mauricio Sebastian",
publisher = "Pontificia Universidad Católica del Perú",
year = "2024"
}
Ultrasound elastography is a noninvasive imaging technique that aims to provide information about the elasticity of biological tissues by evaluating their biomechanical properties. It provides valuable information about their stiffness, which is related to biomechanical changes caused by pathological conditions. This makes it a valuable tool for diagnosing and monitoring the treatment of diseases such as cancer. One of the quantitative elastography methods is based on the use of two vibration sources to generate an interference pattern in the tissue. The shear wave can be visualized in real time by sonoelastography and an inversion scheme must be applied to recover the shear wave velocity from several frames. However, estimators studied in the literature exhibit some limitations such as poor performance in noisy environments, and long acquisition and processing times for real-time applications. In this thesis, two algorithms, based on the Short-Time Fourier Transform and the Continuous Wavelet Transform, are proposed and implemented for shear wave velocity estimation. Homogeneous and heterogeneous gelatin phantoms were fabricated to evaluate the performance of the algorithms under controlled conditions. Sonoelastography experiments were performed at different vibration frequencies to evaluate their accuracy in a range of scenarios. The results show that the developed algorithms are comparable to existing estimators in terms of bias, coefficient of variation, contrast-to-noise ratio and resolution. In homogeneous media, the coefficient of variation remained below 10% for both estimators and, in heterogeneous media, the Continuous Wavelet Transform achieved a contrast-to-noise ratio of 30 dB on average. In general, the algorithms show superior robustness, especially in the presence of poor signal-to-noise ratio and in stiffer tissues with higher shear wave velocities. It was shown that the proposed algorithms do not need the entire sonoelastography video to generate a shear wave velocity map, but only a single frame. This allows real-time visualization of the shear wave velocity, which can benefit various clinical applications.
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