A new clutter rejection algorithm for doppler ultrasound

Abstract

Several strategies, known as clutter or wall Doppler filtering, were proposed to remove the strong echoes produced by stationary or slow moving tissue structures from the Doppler blood flow signal. In this study, the matching pursuit (MP) method is proposed to remove clutter components. The MP method decomposes the Doppler signal into wavelet atoms that are selected in a decreasing energy order. Thus, the high-energy clutter components are extracted first. In the present study, the pulsatile Doppler signal s(n) was simulated by a sum of random-phase sinusoids. Two types of high-amplitude clutter signals were then superimposed on s(n): time-varying low-frequency components, covering systole and early diastole, and short transient clutter signals, distributed within the whole cardiac cycle. The Doppler signals were modeled with the MP method and the most dominant atoms were subtracted from the time-domain signal s(n) until the signal-to-clutter (S/C) ratio reached a maximum. For the low-frequency clutter signal, the improvement in S/C ratio was 19.0 +/- 0.6 dB, and 72.0 +/- 4.5 atoms were required to reach this performance. For the transient clutter signal, ten atoms were required and the maximum improvement in S/C ratio was 5.5 +/- 0.5 dB. The performance of the MP method was also tested on real data recorded over the common carotid artery of a normal subject. Removing 15 atoms significantly improved the appearance of the Doppler sonogram contaminated with low-frequency clutter. Many more atoms (over 200) were required to remove transient clutter components. These results suggest the possibility of using this signal processing approach to implement clutter rejection filters on ultrasound commercial instruments.

An Adaptive Wavelet Method for Clutter Rejection of Doppler Signal: A Simulation Study

“…To explicitly examine the filtering difference form spectrogram, the low frequency part of above spectrograms was locally magnified and presented correspondingly in Fig.2. After the clutter component was removed, the S/C ratio can be calculated in terms of the equation (11) presented in literature [2]. The S/C ratio was 2.66 dB and 2.05 dB by the TIDWT with BSF and the HPF respectively, but the S/C ratio of the simulated contaminated signal was -20dB.…”

“…The approaches applied to remove clutter are known as clutter or wall filter. The conventional methods used by ultrasound manufactures are mainly the static temporal finite and infinite impulse response high pass filters (HPF), and the standard linear regression filters (SLR) [1] [2]. Although the HPF is simple and effective for removing clutter components, there exists an obvious drawback that Doppler signals from the low blood flow falling into clutter frequency region are also simultaneously filtered out.…”

“…In order to preserve the integrity of low blood flow, the wavelet technique has been employed to represent the subtle details in the low frequency region. The matching pursuit (MP) method [2] and the wavelet time-scale domain approach [7] were developed. In the context of color blood flow, the new methods of clutter filtering were developed to pertain to the short duration of the signal.…”

Probabilistic framework for tracking in artifact-prone 3D echocardiograms

“…Stationary clutter rejection methods have been developed mostly for color Doppler flow imaging (Bjaerum et al, 2002;Cloutier et al, 2003;Yoo et al, 2003). For identifying reverberations in normal ultrasound images, Duarte et al (2003) presented an algorithm based on the comparison of power spectra and time-of-flight.…”

Delicate seperation of Doppler blood flow and vessel wall beat signals by using the EEMD-based algorithm

“…The vessel wall signals gravely obstruct the low-frequency blood flow signals, and make it difficult to extract blood flow signals close to the vessel wall exactly. In order to achieve intact blood flow signals, several researches have been proposed variety methods including eigen decomposition [3], blind signal separation [4], and recursive principal component analysis [5] in time domain, as well as matching pursuit [6], wavelet coefficient analysis [7] and short time Fourier transform [8] in frequency domain. Although the accuracy of the blood flow components extracted from the mixed signal has been improved in some extent by using these methods, the veracity and reliability of extraction can not be guaranteed due to the lack of an objective criterion for distinguishing the blood flow and vessel wall signals according to their different properties.…”