Characterization of Tissue Microstructure Scatterer Distribution with Spectral Correlation

1993 ◽  
Vol 15 (3) ◽  
pp. 238-254 ◽  
Author(s):  
T Varghese
1993 ◽  
Vol 15 (3) ◽  
pp. 238-254 ◽  
Author(s):  
Tomy Varghese ◽  
Kevin D. Donohue

Characterization of tissue microstructure from the backscattered ultrasound signal using the spectral autocorrelation (SAC) function provides information about the scatterer distribution in biological tissue. This paper demonstrates SAC capabilities in characterizing periodicities in A-scans due to regularity in the scatterer distribution. The A-scan is modelled as a cyclostationary signal, where the statistical parameters of the signal vary in time with single or multiple periodicities. This periodicity manifests itself as spectral peaks both in the power spectral density (PSD) and in the SAC. Periodicity in the PSD will produce a well defined dominant peak in the cepstrum, which has been used to determine the scatterer spacing. The relationship between the scatterer spacing and the spacing of the spectral peaks is established using a stochastic model of the echo-formation process from biological tissue. The distribution of the scatterers within the microstructure is modelled using a Gamma function, which offers a flexible method of simulating parametric regularity in the scatterer spacing. Simulations of the tissue microstructure for lower orders of regularity indicate that the SAC components reveal information about the scatterer spacing that are not seen in the PSD and the cepstrum. The echo-formation process is tested by simulating microstructure of varying regularity and analyzing their effect on the SAC, PSD and cepstrum. Experimental validation of the simulation results are provided using in vivo scans of the breast and liver tissue that show the presence of significant spectral correlation components in the SAC.


1994 ◽  
Vol 13 (4) ◽  
pp. 497-511 ◽  
Author(s):  
Keith R. Hardwicke ◽  
Gary R. Wilson ◽  
Kevin W. Baugh

Author(s):  
Evren Ozarslan ◽  
Peter J. Basser ◽  
Timothy M. Shepherd ◽  
Peter E. Thelwall ◽  
Baba C. Vemuri ◽  
...  

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2379 ◽  
Author(s):  
Guillermo Rus ◽  
Inas H. Faris ◽  
Jorge Torres ◽  
Antonio Callejas ◽  
Juan Melchor

The adoption of multiscale approaches by the biomechanical community has caused a major improvement in quality in the mechanical characterization of soft tissues. The recent developments in elastography techniques are enabling in vivo and non-invasive quantification of tissues’ mechanical properties. Elastic changes in a tissue are associated with a broad spectrum of pathologies, which stems from the tissue microstructure, histology and biochemistry. This knowledge is combined with research evidence to provide a powerful diagnostic range of highly prevalent pathologies, from birth and labor disorders (prematurity, induction failures, etc.), to solid tumors (e.g., prostate, cervix, breast, melanoma) and liver fibrosis, just to name a few. This review aims to elucidate the potential of viscous and nonlinear elastic parameters as conceivable diagnostic mechanical biomarkers. First, by providing an insight into the classic role of soft tissue microstructure in linear elasticity; secondly, by understanding how viscosity and nonlinearity could enhance the current diagnosis in elastography; and finally, by compounding preliminary investigations of those elastography parameters within different technologies. In conclusion, evidence of the diagnostic capability of elastic parameters beyond linear stiffness is gaining momentum as a result of the technological and imaging developments in the field of biomechanics.


Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2802
Author(s):  
Averyan V. Pushkarev ◽  
Alexey V. Orlov ◽  
Sergey L. Znoyko ◽  
Vera A. Bragina ◽  
Petr I. Nikitin

The ever-increasing use of magnetic particle bioconjugates (MPB) in biosensors calls for methods of comprehensive characterization of their interaction with targets. Label-free optical sensors commonly used for studying inter-molecular interactions have limited potential for MPB because of their large size and multi-component non-transparent structure. We present an easy-to-use method that requires only three 20-min express measurements to determine the key parameters for selection of optimal MPB for a biosensor: kinetic and equilibrium characteristics, and a fraction of biomolecules on the MPB surface that are capable of active targeting. The method also provides a prognostic dependence of MPB targeting efficiency upon interaction duration and sample volume. These features are possible due to joining a magnetic lateral flow assay, a highly sensitive sensor for MPB detection by the magnetic particle quantification technique, and a novel mathematical model that explicitly describes the MPB-target interactions and does not comprise parameters to be fitted additionally. The method was demonstrated by experiments on MPB targeting of cardiac troponin I and staphylococcal enterotoxin B. The validation by an independent label-free technique of spectral-correlation interferometry showed good correlation between the results obtained by both methods. The presented method can be applied to other targets for faster development and selection of MPB for affinity sensors, analytical technologies, and realization of novel concepts of MPB-based biosensing in vivo.


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