Spectral and Scatterer-Size Correlation during Angular Compounding: Simulations and Experimental Studies

Liu, Wu; Zagzebski, James A.; Varghese, Tomy; Gerig, Anthony L.; Hall, Timothy J.

doi:10.1177/016173460602800403

Cited by 13 publications

(14 citation statements)

References 16 publications

(3 reference statements)

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“…Results will be compared for different types of gating windows, window lengths, and different power spectra estimation methods. Moreover, the possibilities of a depth dependence on scatterer size estimations, not reported in previous studies [12, 22, 23] is also considered. This work will discuss the frequency range changes (hence, size estimation changes) over depth.…”

Section: Introductionmentioning

confidence: 99%

Trade-offs in data acquisition and processing parameters for backscatter and scatterer size estimations

Liu

Zagzebski

2010

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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By analyzing backscattered echo signal power spectra and thereby obtaining backscatter coefficient vs. frequency data, the size of sub-resolution scatterers contributing to echo signals can be estimated.Here we investigate tradeoffs in data acquisition and processing parameters for reference phantom based backscatter and scatterer size estimations. RF echo data from a tissue-mimicking test phantom were acquired using a clinical scanner equipped with linear array transducers. One array has a nominal frequency bandwidth of 5-13 MHz and the other 4-9 MHz. Comparison of spectral estimation methods showed that the Welch method provided spectra yielding more accurate and precise backscatter coefficient and scatterer size estimations than spectra computed by applying rectangular, Hanning, or Hamming windows and much reduced computational load than if using the multitaper method. For small echo signal data block sizes, moderate improvements in scatterer size estimations were obtained using a multitaper method but this significantly increases the computational burden.It is critical to average power spectra from lateral A-lines for the improvement of scatterer size estimation. Averaging approximately 10 independent A-lines laterally, with an axial window length 10 times the center frequency wavelength optimized tradeoffs between spatial resolution and the variance of scatterer size estimates. Applying the concept of a time-bandwidth product this suggests using analysis blocks that contain at least 30 independent samples of the echo signal.The estimation accuracy and precision depend on the ka range where k is the wave number and a is the effective scatterer size. This introduces an ROI depth dependency to the accuracy and precision because of preferential attenuation of higher frequency sound waves in tissue-like media. With the 5-13 MHz transducer ka ranged from 0.5 -1.6 for scatterers in the test phantom, which is a favorable range, and the accuracy and precision of scatterer size estimations were both within ~5% using optimal analysis block dimensions. When the 4-9 MHz transducer was used, the ka value ranged from 0.3 to 0.8 -1.1 for the experimental conditions, and the accuracy and precision were found to be ~10% and 10% -25%, respectively. Although the experiments were done with two specific models of transducers on the test phantom, the results can be generalized to similar clinical arrays available from a variety of manufacturers and/or for different size of scatterers with similar ka range.

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Section: Introductionmentioning

confidence: 99%

Trade-offs in data acquisition and processing parameters for backscatter and scatterer size estimations

Liu

Zagzebski

2010

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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“…researches of quantitative ultrasound analysis also have steadily tried to estimate several medical ultrasound parameters in the spectral domain, such as the attenuation coefficient, speed of sound, and backscatter density and size. This is done by converting the received time-domain ultrasound signals into spectral domain signals using FFT (fast Fourier transform) to provide more quantitative information of the soft tissue scanned [1]- [3]. Because the propagated acoustic waves in the soft tissue exhibit frequency-dependent behaviors, it is a common and reasonable approach to analyze the time-domain signals using the Fourier transform [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

“…among various medical ultrasound parameters, the spectral centroid of the backscattered signals is one of the important parameters and has been widely utilized in numerous medical ultrasound applications [3], [5]. This is because it provides the information of attenuation properties under the soft tissues passed through, as well as important information to estimate other parameters for compensating depth-dependent attenuation [1], [7]. To estimate the spectral centroid accurately and consistently, many estimation algorithms have been proposed in the literature and applied to various applications, particularly in the attenuation property estimation of bones [8] and soft tissues [9]- [11].…”

Section: Introductionmentioning

confidence: 99%

Optimization of the autocorrelation weighting function for the time-domain calculation of spectral centroids

Heo

Hur

Kim

2015

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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Spectral centroid from the backscattered ultrasound provides important information about the attenuation properties of soft tissues and Doppler effects of blood flows. Because the spectral centroid is originally determined from the power spectrum of backscattered ultrasound signals in the frequency domain, it is natural to calculate it after converting time-domain signals into spectral domain signals, using the fast Fourier transform (FFT). Recent research, however, derived the time-domain equations for calculating the spectral centroid using a Parseval's theorem, to avoid the calculation of the Fourier transform. The work only presented the final result, which showed that the computational time of the proposed time-domain method was 4.4 times faster than that of the original FFT-based method, whereas the average estimation error was negligible. In this paper, we present the optimal design of the autocorrelation weighting function, which is used for the timedomain spectral centroid estimation process, to reduce the computational time significantly. We also carry out a comprehensive analysis of the computational complexities of the FFTbased and time-domain methods with respect to the length of ultrasound signal segments. The simulation results using numerical phantoms show that, with the optimized autocorrelation weighting function, we only need approximately 3% of the full set of data points. In addition to that, because the proposed optimization technique requires a fixed number of data points to calculate the spectral centroid, the execution time is constant as the length of the data segment increases, whereas the execution time of the conventional FFT-based method is increased. Analysis of the computational complexities between the proposed method and the conventional FFT-based method presents O(N) and O(Nlog2N), respectively.

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“…The radio frequency (RF) data gathered from these ultrasound scans can be used to calculate quantitative ultrasound parameters (QUS) like attenuation, backscatter, scatter size, and acoustic concentration [1][2][3][4][5]. Ultrasonic attenuation is defined as the loss of energy of the ultrasound wave as it propagates in the medium.…”

Section: Introductionmentioning

confidence: 99%

Using speckle statistics to improve attenuation estimates for cervical assessment

Kumar

Bigelow

2014

The Journal of the Acoustical Society of America

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Quantitative ultrasound parameters like attenuation can be used to observe microchanges in the cervix. To give a better estimate of attenuation we can use speckle properties to classify which attenuation estimates are valid and conform to the theory. For fully developed and only one type of scatterer, Rayleigh distribution models the signal envelope. But in tissues as the number of scatterer type increases and the speckle becomes unresolved Rayleigh model fails. Gamma distribution has been empirically shown to be the best fit among all the distributions. Since more than one scatterer type is present for our work we used a mixture of gamma distributions. EM algorithm was used to find the parameters of the mixture and on basis of that the tissue types were segmented from each other based on the criteria of different scattering properties. Attenuation estimates were then calculated for tissues of the same scattering type only. 67 Women underwent Transvaginal scan and the attenuation estimates were calculated for them after segregation of tissues on scattering basis. Attenuation was seen to decrease as the time of delivery came closer.

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Spectral and Scatterer-Size Correlation during Angular Compounding: Simulations and Experimental Studies

Cited by 13 publications

References 16 publications

Trade-offs in data acquisition and processing parameters for backscatter and scatterer size estimations

Trade-offs in data acquisition and processing parameters for backscatter and scatterer size estimations

Optimization of the autocorrelation weighting function for the time-domain calculation of spectral centroids

Using speckle statistics to improve attenuation estimates for cervical assessment

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