Zobrazeno 1 - 10
of 247
pro vyhledávání: '"Sharifzadeh, Mostafa"'
Autor:
Asgariandehkordi, Hojat, Goudarzi, Sobhan, Sharifzadeh, Mostafa, Basarab, Adrian, Rivaz, Hassan
Ultrasound plane wave imaging is a cutting-edge technique that enables high frame-rate imaging. However, one challenge associated with high frame-rate ultrasound imaging is the high noise associated with them, hindering their wider adoption. Therefor
Externí odkaz:
http://arxiv.org/abs/2408.10987
Radio frequency (RF) data contain richer information compared to other data types, such as envelope or B-mode, and employing RF data for training deep neural networks has attracted growing interest in ultrasound image processing. However, RF data is
Externí odkaz:
http://arxiv.org/abs/2308.11833
Ultrasound imaging often suffers from image degradation stemming from phase aberration, which represents a significant contributing factor to the overall image degradation in ultrasound imaging. Frequency-space prediction filtering or FXPF is a techn
Externí odkaz:
http://arxiv.org/abs/2308.11830
One of the primary sources of suboptimal image quality in ultrasound imaging is phase aberration. It is caused by spatial changes in sound speed over a heterogeneous medium, which disturbs the transmitted waves and prevents coherent summation of echo
Externí odkaz:
http://arxiv.org/abs/2308.11149
Phase aberration is one of the primary sources of image quality degradation in ultrasound, which is induced by spatial variations in sound speed across the heterogeneous medium. This effect disrupts transmitted waves and prevents coherent summation o
Externí odkaz:
http://arxiv.org/abs/2303.05747
Autor:
Ma, Junjie, Li, Tianbin, Zhang, Zhen, Shirani Faradonbeh, Roohollah, Sharifzadeh, Mostafa, Ma, Chunchi
Publikováno v:
In Underground Space February 2025 20:140-156
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
Convolutional neural networks (CNNs) have attracted a rapidly growing interest in a variety of different processing tasks in the medical ultrasound community. However, the performance of CNNs is highly reliant on both the amount and fidelity of the t
Externí odkaz:
http://arxiv.org/abs/2109.10353
A common issue in exploiting simulated ultrasound data for training neural networks is the domain shift problem, where the trained models on synthetic data are not generalizable to clinical data. Recently, Fourier Domain Adaptation (FDA) has been pro
Externí odkaz:
http://arxiv.org/abs/2109.09969
While accuracy is an evident criterion for ultrasound image segmentation, output consistency across different tests is equally crucial for tracking changes in regions of interest in applications such as monitoring the patients' response to treatment,
Externí odkaz:
http://arxiv.org/abs/2107.10431