Homodyned K-Distribution Parameter Estimation in Quantitative Ultrasound: Autoencoder and Bayesian Neural Network Approaches

Quantitative ultrasound (QUS) analyzes the ultrasound (US) backscattered data to find the properties of scatterers that correlate with the tissue microstructure. Statistics of the envelope of the backscattered radio frequency (RF) data can be utilized to estimate several QUS parameters. Different di...

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Vydané v:	IEEE transactions on ultrasonics, ferroelectrics, and frequency control Ročník 71; číslo 3; s. 354 - 365
Hlavní autori:	Tehrani, Ali K. Z., Cloutier, Guy, Tang, An, Rosado-Mendez, Ivan M., Rivaz, Hassan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.03.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Autoencoder Backscattering Clustering Coherent scattering deep learning (DL) homodyned K-distribution Hyperplanes In vivo methods and tests K-distribution Kurtosis Mathematical models Neural networks Noise reduction Optimization Optimization methods Parameter estimation quantitative ultrasound (QUS) Radio frequency Scattering Signal to noise ratio Ultrasonic imaging
ISSN:	0885-3010, 1525-8955, 1525-8955
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