scholarly journals Towards Understanding the Nature of High Frequency Ultrasound Backscatter from Cells and Tissues: an Investigation of Backscatter Power Spectra from Different Concentrations of Cells of Different Sizes

2021 ◽  
Author(s):  
Michael C. Kolios ◽  
G. J. Czarnota ◽  
A. E. Worthington ◽  
A. Giles ◽  
A. S. Tunis ◽  
...  
Keyword(s):  
High Frequency ◽  
Power Spectra ◽  
High Frequency Ultrasound ◽  
Ultrasound Backscatter ◽  
Frequency Ultrasound

Towards Understanding the Nature of High Frequency Ultrasound Backscatter from Cells and Tissues: an Investigation of Backscatter Power Spectra from Different Concentrations of Cells of Different Sizes

scholarly journals Towards Understanding the Nature of High Frequency Ultrasound Backscatter from Cells and Tissues: an Investigation of Backscatter Power Spectra from Different Concentrations of Cells of Different Sizes

2021 ◽  
Author(s):  
Michael C. Kolios ◽  
G. J. Czarnota ◽  
A. E. Worthington ◽  
A. Giles ◽  
A. S. Tunis ◽  
...  
Keyword(s):  
High Frequency ◽  
Power Spectra ◽  
High Frequency Ultrasound ◽  
Ultrasound Backscatter ◽  
Frequency Ultrasound

Towards Understanding the Nature of High Frequency Ultrasound Backscatter from Cells and Tissues: an Investigation of Backscatter Power Spectra from Different Concentrations of Cells of Different Sizes

3-D High-Frequency Ultrasound Backscatter Analysis of Human Articular Cartilage

2014 ◽  
Vol 40 (1) ◽  
pp. 244-257 ◽  
Author(s):  
Nils Männicke ◽  
Martin Schöne ◽  
Matthias Gottwald ◽  
Felix Göbel ◽  
Michael L. Oelze ◽  
...  
Keyword(s):  
Articular Cartilage ◽  
High Frequency ◽  
Human Articular Cartilage ◽  
High Frequency Ultrasound ◽  
Ultrasound Backscatter ◽  
Frequency Ultrasound

Species-Independent Modeling of High-Frequency Ultrasound Backscatter in Hyaline Cartilage

2016 ◽  
Vol 42 (6) ◽  
pp. 1375-1384 ◽  
Author(s):  
Nils Männicke ◽  
Martin Schöne ◽  
Jukka Liukkonen ◽  
Dominik Fachet ◽  
Satu Inkinen ◽  
...  
Keyword(s):  
High Frequency ◽  
Hyaline Cartilage ◽  
High Frequency Ultrasound ◽  
Ultrasound Backscatter ◽  
Frequency Ultrasound

scholarly journals Effect of articular cartilage proteoglycan depletion on high frequency ultrasound backscatter

2002 ◽  
Vol 10 (7) ◽  
pp. 535-541 ◽  
Author(s):  
B. Pellaumail ◽  
A. Watrin ◽  
D. Loeuille ◽  
P. Netter ◽  
G. Berger ◽  
...  
Keyword(s):  
Articular Cartilage ◽  
High Frequency ◽  
High Frequency Ultrasound ◽  
Cartilage Proteoglycan ◽  
Ultrasound Backscatter ◽  
Frequency Ultrasound

High frequency ultrasound backscatter imaging

2002 ◽  
Author(s):  
F.S. Foster ◽  
C.J. Pavlin ◽  
G.R. Lockwood ◽  
L.K. Ryan ◽  
K. Harasiewicz ◽  
...  
Keyword(s):  
High Frequency ◽  
High Frequency Ultrasound ◽  
Ultrasound Backscatter ◽  
Frequency Ultrasound

scholarly journals Design and Application of Signal Modeling, Segmentation and Classification Methods for High-Frequency Ultrasound Backscatter Signals

2021 ◽  
Author(s):  
Noushin R. Farnoud
Keyword(s):  
Machine Learning ◽  
High Frequency ◽  
Monitoring Program ◽  
Least Square ◽  
Machine Learning Techniques ◽  
Square Lattice ◽  
High Frequency Ultrasound ◽  
Recursive Least Square ◽  
Ultrasound Backscatter ◽  
Frequency Ultrasound

In this study, we explore the possibility of monitoring program cell death (apoptosis) and classifying clusters of apoptotic cells based on the changes in high frequency ultrasound backscatter signals from these cells. One of the hallmarks of cancer is that the fail [sic] in the apoptosis mechanism in cells. Therefore this research carries the promise of designing more refined and more effective cancer therapies. The ultrasound signals are modeled through the Autoregressive (AR) modeling technique. The proper model order is calculated by tracking the error criteria derived from statistical properties of the original and modeled signal. In the next stage, five machine learning classifiers are developed to classify backscatter signals based on their AR coefficients. In clinical applications ultrasound backscatter signals from tissues and tumors are most likely to be non-stationary. Therefore analyzing such signals requires signal segmentation techniques. We developed recursive least square lattice filter for adaptive segmentation of ultrasound backscatter signals from multiple cell types into blocks of stationary segments, and model and classify the segments individually. In this thesis we demonstrate the accuracy of modeling, segmentation and classification techniques to detect signals from different cell pellets based on the signal processing and machine learning techniques.

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