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pro vyhledávání: '"Tehrani, Ali K. Z."'
Homodyned K-distribution (HK-distribution) parameter estimation in quantitative ultrasound (QUS) has been recently addressed using Bayesian Neural Networks (BNNs). BNNs have been shown to significantly reduce computational time in speckle statistics-
Externí odkaz:
http://arxiv.org/abs/2409.11583
Quantitative ultrasound (QUS) analyzes the ultrasound backscattered data to find the properties of scatterers that correlate with the tissue microstructure. Statistics of the envelope of the backscattered radiofrequency (RF) data can be utilized to e
Externí odkaz:
http://arxiv.org/abs/2401.11006
Tracking the displacement between the pre- and post-deformed radio-frequency (RF) frames is a pivotal step of ultrasound elastography, which depicts tissue mechanical properties to identify pathologies. Due to ultrasound's poor ability to capture inf
Externí odkaz:
http://arxiv.org/abs/2305.20059
Quantitative ultrasound (QUS) aims to find properties of scatterers which are related to the tissue microstructure. Among different QUS parameters, scatterer number density has been found to be a reliable biomarker for detecting different abnormaliti
Externí odkaz:
http://arxiv.org/abs/2302.12901
Convolutional Neural Networks (CNN) have shown promising results for displacement estimation in UltraSound Elastography (USE). Many modifications have been proposed to improve the displacement estimation of CNNs for USE in the axial direction. Howeve
Externí odkaz:
http://arxiv.org/abs/2212.08740
Autor:
Tehrani, Ali K. Z., Rivaz, Hassan
Convolutional Neural Networks (CNN) have been employed for displacement estimation in ultrasound elastography (USE). High-quality axial strains (derivative of the axial displacement in the axial direction) can be estimated by the proposed networks. I
Externí odkaz:
http://arxiv.org/abs/2211.00172
Quantitative ultrasound (QUS) allows estimating the intrinsic tissue properties. Speckle statistics are the QUS parameters that describe the first order statistics of ultrasound (US) envelope data. The parameters of Homodyned K-distribution (HK-distr
Externí odkaz:
http://arxiv.org/abs/2211.00175
Quantitative Ultrasound (QUS) provides important information about the tissue properties. QUS parametric image can be formed by dividing the envelope data into small overlapping patches and computing different speckle statistics such as parameters of
Externí odkaz:
http://arxiv.org/abs/2206.04145
Autor:
Tehrani, Ali K. Z., Rivaz, Hassan
Displacement estimation is a critical step of virtually all Ultrasound Elastography (USE) techniques. Two main features make this task unique compared to the general optical flow problem: the high-frequency nature of ultrasound radio-frequency (RF) d
Externí odkaz:
http://arxiv.org/abs/2206.02225
The performance of ultrasound elastography (USE) heavily depends on the accuracy of displacement estimation. Recently, Convolutional Neural Networks (CNN) have shown promising performance in optical flow estimation and have been adopted for USE displ
Externí odkaz:
http://arxiv.org/abs/2201.13340