Identifying acoustic scattering sources in normal renal parenchyma from the anisotropy in acoustic properties

Insana, Michael F.; Hall, Timothy J.; Fishback, James L.

doi:10.1016/0301-5629(91)90032-r

Cited by 117 publications

(88 citation statements)

References 27 publications

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“…The time delays for the echoes from the reflector (T m ) and from both interfaces of the sample (t 1 and t 2 ) were measured. The sound speed (c m ) was then calculated from (8) where T w is the propagation time measured when the sample is removed. The reference speed of sound in water, c w , was 1481 m/s.…”

Section: Laboratorymentioning

confidence: 99%

Interlaboratory Comparison of Ultrasonic Backscatter Coefficient Measurements From 2 to 9 MHz

Wear

Stiles²,

Frank³

et al. 2005

Journal of Ultrasound in Medicine

139

119

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Objective. As are the attenuation coefficient and sound speed, the backscatter coefficient is a fundamental ultrasonic property that has been used to characterize many tissues. Unfortunately, there is currently far less standardization for the ultrasonic backscatter measurement than for the other two, as evidenced by a previous American Institute of Ultrasound in Medicine (AIUM)-sponsored interlaboratory comparison of ultrasonic backscatter, attenuation, and speed measurements (J Ultrasound Med 1999; 18:615-631). To explore reasons for these disparities, the AIUM Endowment for Education and Research recently supported this second interlaboratory comparison, which extends the upper limit of the frequency range from 7 to 9 MHz. Methods. Eleven laboratories were provided with standard test objects designed and manufactured at the University of Wisconsin (Madison, WI). Each laboratory was asked to perform ultrasonic measurements of sound speed, attenuation coefficients, and backscatter coefficients. Each laboratory was blinded to the values of the ultrasonic properties of the test objects at the time the measurements were performed. Results. Eight of the 11 laboratories submitted results. The range of variation of absolute magnitude of backscatter coefficient measurements was about 2 orders of magnitude. If the results of 1 outlier laboratory are excluded, then the range is reduced to about 1 order of magnitude. Agreement regarding frequency dependence of backscatter was better than reported in the previous interlaboratory comparison. For example, when scatterers were small compared with the ultrasonic wavelength, experimental frequency-dependent backscatter coefficient data obtained by the participating laboratories were usually consistent with the expected Rayleigh scattering behavior (proportional to frequency to the fourth power). Conclusions. Greater standardization of backscatter measurement methods is needed. Measurements of frequency dependence of backscatter are more consistent than measurements of absolute magnitude.

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Section: Laboratorymentioning

confidence: 99%

Interlaboratory Comparison of Ultrasonic Backscatter Coefficient Measurements From 2 to 9 MHz

Wear

Stiles²,

Frank³

et al. 2005

Journal of Ultrasound in Medicine

139

119

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“…3 Former studies to validate the capability of US spectrum analysis in quantifying the dimensions and concentrations of acoustic back-scatterers within biological tissues indicated that the slopes of the linear models reflect the dimensions of the ultrasonic scatterers, and the intercepts encode both the dimensions and concentrations of the scatterers within the ROI. [6][7][8] PA signals, i.e., the US waves generated from light illumination of optically absorbing objects, demonstrate tissue contrast completely different from those revealed by US pulse-echo signals. Examination of PA power spectrum, employing the similar methods as in US spectrum analysis, may facilitate characterization of microscopic features in a biological tissue based on the tissue optical properties.…”

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confidence: 93%

Photoacoustic spectrum analysis for microstructure characterization in biological tissue: A feasibility study

Dar

Tao

et al. 2012

Applied Physics Letters

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This study investigates the feasibility of characterizing the microstructures within a biological tissue by analyzing the frequency spectrum of the photoacoustic signal from the tissue. Hypotheses are derived from theoretical analyses on the relationships between the dimensions/concentrations of the photoacoustic sources within the region-of-interest and the linear model fitted to the power spectra of photoacoustic signals. The hypotheses are validated, following the procedures of ultrasound spectrum analysis, by simulations and experiments with phantoms fabricated by embedding the polyethylene microspheres in porcine gelatin, indicating that photoacoustic spectrum analysis could be a potential tool for characterizing microstructures in biological samples. Photoacoustic (PA) imaging is a non-invasive modality that physically combines the high resolution of ultrasonography and the functional contrast of optical imaging. Although, in some cases, the macroscopic transitions of tissue optical properties in PA images can be evaluated through visual observation, the less significant contrast fluctuations within each seemingly homogeneous region is usually ignored. These small signal fluctuations, excluding the system noises, actually encode the dimensions and optical absorption contrasts of the microstructures within the imaged domain. In former research, the extraction and visualization of the microstructure information from ultrasound (US) signals has been extensively investigated using the methods of spectrum analysis. US spectrum analysis has shown promise in the detection and characterization of cancer 1,2 as well as diseased tissues in liver 3 and blood vessel. 4 The principle of US spectrum analysis is to characterize the acoustic scattering properties of the microstructures within the region of interest (ROI) by observing several key factors, e.g., slope, intercept, and midband fit, of the linear models fitted to the truncated signal power spectra within a predetermined frequency interval. 5 The utilization of Linear model is due to the fact that the spectra of US signals in dB usually monotonically increase or decrease following quasi-linear shapes.3 Former studies to validate the capability of US spectrum analysis in quantifying the dimensions and concentrations of acoustic back-scatterers within biological tissues indicated that the slopes of the linear models reflect the dimensions of the ultrasonic scatterers, and the intercepts encode both the dimensions and concentrations of the scatterers within the ROI. [6][7][8]

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“…Thus, from Eq. (6), the corresponding ensembleaveraged 2-D power spectrum is (8) We now treat the specific case in which T(x, y, z) exhibits wide-sense stationarity. Under the wide-sense stationarity condition, the mean value of a process is independent of the absolute position and is only a function of the relative position.…”

Section: The Initial Assumptions Of the Model Arementioning

confidence: 99%

“…[5][6][7] Insana and Hall studied the acoustic backscatter of kidney microstructures. 8,9 Donohue et al…”

Section: Introductionmentioning

confidence: 99%

“…10 Among these groups, anisotropy in attenuation and in backscatter was reported for tissues, e.g., skeletal muscles, 11 myocardium, 7,12 and kidney. 8,13 In the early 1980s, Lizzi et al developed a calibrated (normalized) 1-D spectrum method to quantitatively characterize tissue in terms of its physical properties. 14 This technique utilizes the radio-frequency (rf) echo signals measured directly at conventional clinical ultrasound probes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ultrasonic tissue characterization via 2‐D spectrum analysis: Theory and in vitro measurements

Liu¹,

Lizzi²,

Ketterling³

et al. 2007

Medical Physics

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A theoretical model is described for application in ultrasonic tissue characterization using a calibrated 2-D spectrum analysis method. This model relates 2-D spectra computed from ultrasonic backscatter signals to intrinsic physical properties of tissue microstructures, e.g., size, shape, and acoustic impedance. The model is applicable to most clinical diagnostic ultrasound systems. Two experiments employing two types of tissue architectures, spherical and cylindrical scatterers, are conducted using ultrasound with center frequencies of 10 and 40 MHz, respectively. Measurements of a tissue-mimicking phantom with an internal suspension of microscopic glass beads are used to validate the theoretical model. Results from in vitro muscle fibers are presented to further elucidate the utility of 2-D spectrum analysis in ultrasonic tissue characterization.

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Identifying acoustic scattering sources in normal renal parenchyma from the anisotropy in acoustic properties

Cited by 117 publications

References 27 publications

Interlaboratory Comparison of Ultrasonic Backscatter Coefficient Measurements From 2 to 9 MHz

Interlaboratory Comparison of Ultrasonic Backscatter Coefficient Measurements From 2 to 9 MHz

Photoacoustic spectrum analysis for microstructure characterization in biological tissue: A feasibility study

Ultrasonic tissue characterization via 2‐D spectrum analysis: Theory and in vitro measurements

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