Time-domain calculation of spectral centroid from backscattered ultrasound signals

Kim, Hyungsuk; Heo, Seo Weon

doi:10.1109/tuffc.2012.2309

Cited by 9 publications

(7 citation statements)

References 22 publications

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“…In many of the simulation studies for attenuation estimation, a single scatterer size and an SND greater than 10 per mm is often used (Kim and Varghese, 2008, 2007; Kim et al, 2008; Kim, H. and Heo, S.W., 2012; Labyed and Bigelow, 2011; Nam et al, 2011a; Omari et al, 2011, 2013). This is done to yield Rayleigh scattering statistics.…”

Section: Introductionmentioning

confidence: 99%

Scatterer Number Density Considerations in Reference Phantom-Based Attenuation Estimation

Rubert

Varghese

2014

Ultrasound in Medicine & Biology

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Attenuation estimation and imaging has the potential to be a valuable tool for tissue characterization, particularly for indicating the extent of thermal ablation therapy in the liver. Often the performance of attenuation estimation algorithms is characterized with numerical simulations or tissue mimicking phantoms containing a high scatterer number density (SND). This ensures an ultrasound signal with a Rayleigh distributed envelope and an SNR approaching 1.91. However, biological tissue often fails to exhibit Rayleigh scattering statistics. For example, across 1,647 ROI's in 5 ex vivo bovine livers we find an envelope SNR of 1.10 ± 0.12 when imaged with the VFX 9L4 linear array transducer at a center frequency of 6.0 MHz on a Siemens S2000 scanner. In this article we examine attenuation estimation in numerical phantoms, TM phantoms with variable SND's, and ex vivo bovine liver prior to and following thermal coagulation. We find that reference phantom based attenuation estimation is robust to small deviations from Rayleigh statistics. However, in tissue with low SND, large deviations in envelope SNR from 1.91 lead to subsequently large increases in attenuation estimation variance. At the same time, low SND is not found to be a significant source of bias in the attenuation estimate. For example, we find the standard deviation of attenuation slope estimates increases from 0.07 dB/cm MHz to 0.25 dB/cm MHz as the envelope SNR decreases from 1.78 to 1.01 when estimating attenuation slope in TM phantoms with a large estimation kernel size (16 mm axially by 15 mm laterally). Meanwhile, the bias in the attenuation slope estimates is found to be negligible (< 0.01 dB/cm MHz). We also compare results obtained with reference phantom based attenuation estimates in ex vivo bovine liver and thermally coagulated bovine liver.

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Section: Introductionmentioning

confidence: 99%

Scatterer Number Density Considerations in Reference Phantom-Based Attenuation Estimation

Rubert

Varghese

2014

Ultrasound in Medicine & Biology

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“…attenuation estimation performances, including computational time, estimation accuracy and precision, of the FFT-based spectral-domain method and the time-domain method using an optimized weighting function were previously reported by the authors in [13]. In summary, simulation results using numerical phantoms showed that the time-domain method was approximately 4.4 times faster on average compared with the FFT-based method for various block sizes (from 3 mm × 3 mm to 6 mm × 6 mm) and overlap ratios (from 50 to 90%).…”

Section: B Comparison Of Estimation Performances Using the Optimizedmentioning

confidence: 86%

“…In a previous work by the authors in [13], the calculation of the spectral centroid in the discrete time domain using Parseval's theorem is derived as follows: …”

Section: Review Of the Time-domain Relationsmentioning

confidence: 99%

“…When we increase the overlap ratio of adjacent rF segments (typically in axial and lateral directions) using the short-time Fourier transform technique [12] to improve the spatial resolution of the final analysis, the number of segments to be calculated is exponentially increased. recent research by the authors in [13], however, derived the time-domain equations for calculating the spectral centroid in the continuous and discrete time domains. This work utilized Parseval's theorem and the hilbert transform theory to avoid the calculation of the Fourier transform.…”

Section: Introductionmentioning

confidence: 99%

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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 spectral centroid is one of physical parameters in the timbre which is an attribute of subjective experience for a sound stimulus [8]. Timbre can distinguish two sounds of equal loudness and tone, so the spectral centroid is more for voice signal recognition [9][10][11]. This paper introduces spectral centroid to electromagnetic signal processing because we found that spectral centroid can describe the special characteristics of video leaking signal.…”

Section: Introductionmentioning

confidence: 99%

A low-cost and efficient method of determining the best frequency band for video leaking signal reconstruction

Sun

Shi

Wei

et al. 2014

The 2014 2nd International Conference on Systems and Informatics (ICSAI 2014)

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Determining the best frequency band for reconstructing display video leaking signal is the key problem in TEMPEST, which is the technology of electromagnetic leaking research. To solve such problem, a novel algorithm based on spectral centroid has been developed. Using the property that spectral centroid can accurately identify the signal energy center in frequency domain, the proposed algorithm can find best frequency band automatically. The uniformity degree of spectral centroid spacing distribution is defined to find the best frequency band which has the highest signal to noise ratio (SNR). Thus the video leaking signal reconstruction can be realized efficiently without expensive equipment.

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Time-domain calculation of spectral centroid from backscattered ultrasound signals

Cited by 9 publications

References 22 publications

Scatterer Number Density Considerations in Reference Phantom-Based Attenuation Estimation

Scatterer Number Density Considerations in Reference Phantom-Based Attenuation Estimation

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

A low-cost and efficient method of determining the best frequency band for video leaking signal reconstruction

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