Comparison of the performance of three maximum doppler frequency estimators coupled with different spectral estimation methods

Marasek, Krzysztof; Nowicki, Andrzej

doi:10.1016/0301-5629(94)90111-2

Cited by 30 publications

(16 citation statements)

References 20 publications

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“…The sensitivity of the proposed method with respect to the errors produced by the noise was investigated by introducing the K, and Y variables see (6), and rearranging the model like in (8). as reported in Fig.…”

Section: A Sensitivity Analysismentioning

confidence: 99%

“…The received doppler spectrum is composed by the contributions of all the scatterers moving in the investigated sample volume (sV) [5]. at present, the first step in maximum velocity detection is setting a threshold that intersects the doppler power spectrum at a frequency that is assumed to be the doppler frequency corresponding to the peak velocity in the insonated flow; this is usually the highest frequency detectable above noise [6].…”

Section: Introductionmentioning

confidence: 99%

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Accurate blood peak velocity estimation using spectral models and vector doppler

Ricci

Vilkomerson

Matera

et al. 2015

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

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Ultrasound blood peak velocity estimates are routinely used for diagnostics, such as the grading of a stenosis. The peak velocity is typically assessed from the Doppler spectrum by locating the highest frequency detectable from noise. The selected frequency is then converted to velocity by the Doppler equation. This procedure contains several potential sources of error: the frequency selection is noise dependent and sensitive to the spectral broadening, which, in turn, is affected by the Doppler angle uncertainty. The result is, often, an inaccurate estimate. In this work we propose a new method that removes the aforementioned errors. The frequency is selected by exploiting a mathematical model of the Doppler spectrum that has recently been introduced. When a very large sample volume is used, which includes all the vessel section, the model is capable of predicting the exact threshold to be used without the need of broadening compensation. The angle ambiguity is solved by applying the threshold to the Doppler spectra measured from two different directions, according to the vector Doppler technique. The proposed approach has here been validated through Field II simulations, phantom experiments, and tests on volunteers by using defocused waves to insonify a large region from a linear array probe. A mean error lower than 1% and a mean coefficient of variability lower than 5% were measured in a variety of experimental conditions.

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Section: A Sensitivity Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Accurate blood peak velocity estimation using spectral models and vector doppler

Ricci

Vilkomerson

Matera

et al. 2015

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

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show abstract

“…Up to 200 subsequent spectral profiles are ensemble-averaged to reduce the spectral variance (Evans et al 2000). For each of the 64 averaged spectra, related to the 64 corresponding depths, the peak frequency is estimated through the threshold method (Marasek et al 1994). A polynomial fit of the 64 experimental points is carried out through the LabVIEW ™ "HouseHolder" function.…”

Section: Acquisition-processing Systemmentioning

confidence: 99%

Effect of vessel curvature on Doppler derived velocity profiles and fluid flow

Krams¹,

Bambi²,

Guidi³

et al. 2005

Ultrasound in Medicine & Biology

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“…The detected frequency must then be compensated for spectral broadening by a factor depending on the transducer geometry and doppler angle [3]- [6]. such heuristic methods provide peak velocities that differ for differing signal-noise levels and transducer configurations, and therefore provide unreliable velocity determinations [7].…”

Section: Introductionmentioning

confidence: 99%

Finding the peak velocity in a flow from its doppler spectrum

Vilkomerson

Ricci

Tortoli

2013

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

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The signal backscattered by blood cells crossing a sample volume produces a Doppler power spectrum determined by the scatterers¿ velocity distribution. Because of intrinsic spectral broadening, the peak Doppler frequency observed does not correspond to the peak velocity in the flow. Several methods have been proposed for estimating the maximum velocity component--an important clinical parameter--but these methods are approximate, based on heuristic thresholds that can be inaccurate and strongly affected by noise. Reported here is a method of modeling the Doppler power spectrum of a flow, and from that model, determining what Doppler frequency on the descending slope of the power spectrum corresponds to the peak velocity in the insonated flow. It is shown that, for a fully insonated flow with a parabolic velocity distribution, the peak velocity corresponds to the Doppler frequency at the half-power point on that slope. The method is demonstrated to be robust with regard to the effects of noise and valid for a wide range of acquisition parameters. Experimental maximum velocity measurements on steady flows with rates between 100 and 300 mL/min (peak velocity range 6.6 cm/s to 19.9 cm/s) show a mean bias error that is smaller than 1%.

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Comparison of the performance of three maximum doppler frequency estimators coupled with different spectral estimation methods

Cited by 30 publications

References 20 publications

Accurate blood peak velocity estimation using spectral models and vector doppler

Accurate blood peak velocity estimation using spectral models and vector doppler

Effect of vessel curvature on Doppler derived velocity profiles and fluid flow

Finding the peak velocity in a flow from its doppler spectrum

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