Clutter Filtering Research Articles

Eigen-based clutter filter design for ultrasound color flow imaging: a review

Proper suppression of tissue clutter is a prerequisite for visualizing flow accurately in ultrasound color flow imaging. Among various clutter suppression methods, the eigen-based filter has shown potential because it can theoretically adapt its stopband to the actual clutter characteristics even when tissue motion is present. This paper presents a formative review on how eigen-based filters should be designed to improve their practical efficacy in adaptively suppressing clutter without affecting the blood flow echoes. Our review is centered around a comparative assessment of two eigen-filter design considerations: 1) eigen-component estimation approach (single-ensemble vs. multi-ensemble formulations), and 2) filter order selection mechanism (eigenvalue-based vs. frequencybased algorithms). To evaluate the practical efficacy of existing eigen-filter designs, we analyzed their clutter suppression level in two in vivo scenarios with substantial tissue motion (intra-operative coronary imaging and thyroid imaging). Our analysis shows that, as compared with polynomial regression filters (with or without instantaneous clutter downmixing), eigen-filters that use a frequency-based algorithm for filter order selection generally give Doppler power images with better contrast between blood and tissue regions. Results also suggest that both multi-ensemble and single-ensemble eigen-estimation approaches have their own advantages and weaknesses in different imaging scenarios. It may be beneficial to develop an algorithmic way of defining the eigen-filter formulation so that its performance advantages can be better realized.

FISST-SLAM: Finite Set Statistical Approach to Simultaneous Localization and Mapping

The solution to the problem of mapping an environment and at the same time using this map to localize (the simultaneous localization and mapping, SLAM, problem) is a key prerequisite in the synthesis of truly autonomous vehicles. By far the most common formulation of the SLAM problem is founded on a vector based stochastic framework, where the sensor models and the vehicle models are represented in state-space form and the joint posterior or its statistics are obtained based on recursive Bayesian estimation. All of these SLAM solutions leading from the stochastic vector state-space approach require that we solve certain parallel problems in each recursion. These include effective solutions to the problems of data association, feature extraction, clutter filtering, and landmark or map management. In this paper, we propose an alternative framework based on finite set statistics (FISST), where the SLAM problem is reformulated so that the landmark map and the measurements are represented using random finite sets and the landmark map is jointly estimated with the vehicle state vector, whilst explicitly accounting for measurement detection uncertainty, data-association uncertainty, false alarms and map management in the SLAM filter framework. Similar to FastSLAM, the proposed formulation is based on a factorization of the full SLAM posterior into a product of the vehicle trajectory posterior and the landmark map posterior conditioned on the vehicle trajectory. The vehicle trajectory posterior is then estimated using a particle filter and the map posterior conditioned on the vehicle trajectory via a sequential Monte Carlo (SMC) implementation of the probability hypothesis density (PHD) filter. Simulation results of the proposed algorithm are presented and benchmarked against FastSLAM to demonstrate the effectiveness and improved performance of the FISST-SLAM in the presence of significant clutter.

Adaptive clutter rejection for ultrasound color flow imaging based on recursive eigendecomposition

In the conventional eigenfilter used to reject clutter components of ultrasound color flow imaging, input samples are required to be statistically stationary. However, clutter movements may vary over the depth of the imaged area, which makes the eigenfilter less efficient. In the current study, a novel clutter rejection method is proposed based on the recursive eigendecomposition algorithm. In this method, the current eigenvector matrix of the ultrasound echo correlation matrix, which will be used to construct the clutter subspace, is determined by previous eigenvector matrices and the current input. After the estimated clutter signal is obtained by projecting the original input into the clutter space, each filtered output is eventually obtained by subtracting the estimated clutter signal from the original input. This procedure is iterated for each sample volume along the depth. During the updating process, a forgetting factor is introduced to determine proper weights for different inputs. Simulated data in 3 situations and in vivo data collected from human carotid arteries are used to compare the proposed method with other popular clutter filters. Results show that the proposed method can achieve the most accurate velocity profiles in all simulation situations and introduces the fewest velocity artifacts in the tissue region in the in vivo experiment.

Graphics processing unit-based high-frame-rate color doppler ultrasound processing

Color Doppler ultrasound is a routinely used diagnostic tool for assessing blood flow information in real time. The required signal processing is computationally intensive, involving autocorrelation, linear filtering, median filtering, and thresholding. Because of the large amount of data and high computational requirement, color Doppler signal processing has been mainly implemented on custom-designed hardware, with software-based implementation--particularly on a general-purpose CPU--not being successful. In this paper, we describe the use of a graphics processing unit for implementing signal-processing algorithms for color Doppler ultrasound that achieves a frame rate of 160 fps for frames comprising 500 scan lines x 128 range samples, with each scan line being obtained from an ensemble size of 8 with an 8-tap FIR clutter filter.

Weather Radar Ground Clutter. Part II: Real-Time Identification and Filtering

Abstract The identification and mitigation of anomalous propagation (AP) and normal propagation (NP) ground clutter is an ongoing problem in radar meteorology. Scatter from ground-clutter targets routinely contaminates radar data and masks weather returns causing poor data quality. The problem is typically mitigated by applying a clutter filter to all radar data, but this also biases weather data at near-zero velocity. Modern radar processors make possible the real-time identification and filtering of AP clutter. A fuzzy logic algorithm is used to distinguish between clutter echoes and precipitation echoes and, subsequently, a clutter filter is applied to those radar resolution volumes where clutter is present. In this way, zero-velocity weather echoes are preserved while clutter echoes are mitigated. Since the radar moments are recalculated from clutter-filtered echoes, the underlying weather echo signatures are revealed, thereby significantly increasing the visibility of weather echo. This paper describes the fuzzy logic algorithm, clutter mitigation decision (CMD), for clutter echo identification. A new feature field, clutter phase alignment (CPA), is introduced and described. A detailed discussion of CPA is given in Part I of this paper. The CMD algorithm is illustrated with experimental data from the Denver Next Generation Weather Radar (NEXRAD) at the Denver, Colorado, Front Range Airport (KFTG); and NCAR’s S-band dual-polarization Doppler radar (S-Pol).

Weather Radar Ground Clutter, Part II: Real Time Identification and Filtering

The identification and mitigation of anomalous propagation (AP) and normal propagation (NP) ground clutter is an ongoing problem in radar meteorology. Scatter from ground-clutter targets routinely contaminates radar data and masks weather returns causing poor data quality. The problem is typically mitigated by applying a clutter filter to all radar data, but this also biases weather data at near-zero velocity. Modern radar processors make possible the real-time identification and filtering of AP clutter. A fuzzy logic algorithm is used to distinguish between clutter echoes and precipitation echoes and, subsequently, a clutter filter is applied to those radar resolution volumes where clutter is present. In this way, zero-velocity weather echoes are preserved while clutter echoes are mitigated. Since the radar moments are recalculated from clutter-filtered echoes, the underlying weather echo signatures are revealed, thereby significantly increasing the visibility of weather echo. This paper describes the fuzzy logic algorithm, clutter mitigation decision (CMD), for clutter echo identification. A new feature field, clutter phase alignment (CPA), is introduced and described. A detailed discussion of CPA is given in Part I of this paper. The CMD algorithm is illustrated with experimental data from the Denver Next Generation Weather Radar (NEXRAD) at the Denver, Colorado, Front Range Airport (KFTG); and NCAR’s S-band dual-polarization Doppler radar (S-Pol).

Quantification of valvular regurgitation area and geometry using HPRF 3-D doppler

It is important to determine the severity of valvular regurgitation accurately because surgery is indicated only in severe regurgitations. The evaluation of, for example, mitral regurgitation is complex, and the current methods have limitations. We have developed a 3-D Doppler method to estimate the cross-sectional area and the geometry of a regurgitant jet at the vena contracta just downstream from the actual orifice. The back-scattered Doppler signal from multiple beams distributed over the area of interest was measured. The received power from these beams was then calibrated using both a priori knowledge of the lateral extent of the beams and a reference beam that was completely enclosed by the vena contracta. To isolate the Doppler signal received from the core of a regurgitant jet, a high pulse repetition frequency and a steep clutter filter are required. The method has been implemented and verified by computer simulations and by in vitro experiments using a pulsatile flow phantom and prosthetic valves with a range of holes. We were able to distinguish between mild, moderate, and severe valvular regurgitation. We were also able to quantify the regurgitational area as well as show the geometry of the regurgitation.

Using the Existing Spectral Clutter Filter With the Nonuniformly Spaced Time Series Data in Weather Radar

The National Weather Service (NWS) has a network of weather surveillance Doppler radars, WSR-88D, that is routinely upgraded to reflect current technological and engineering advances. During the last decade, the staggered pulse repetition time (SPRT) was proposed to mitigate range/velocity ambiguity in WSR-88D; however, it could not be used at lowest elevation tilts due to the inability to filter ground clutter from the nonuniformly spaced time series. A complicated spectral procedure was developed to address this issue, but the procedure was derived for a specific set of data acquisition parameters and used a lookup table to provide a value for the clutter spectrum width. Attempts to use SPRT with different sets of acquisition parameters always led to the degradation of filtering. It is proposed here that the clutter spectrum width be estimated by using Gaussian model adaptive processing (GMAP) that is currently used by NWS for uniformly sampled time series. GMAP is an adaptive ground clutter filter that performs an iterative fit of a Gaussian curve to the spectral coefficients identified as being due to ground clutter. GMAP cannot be used directly with the SPRT spectrum because of multiple clutter replicas that are due to staggered nonuniform sampling. However, the elements of GMAP can be exploited and used in the SPRT procedure. This letter presents how the elements of GMAP can be used with the SPRT data to provide successful clutter filtering at lowest elevation tilts.

Phase-Based Clutter Identification in Spectra of Weather Radar Signals

A novel method for suppression of ground clutter (GC) in weather radar is presented. The novel identification scheme is entirely phase based, unlike power-based schemes that are generally used. GC contributions to the Doppler spectrum are identified from the differential phase between complex spectral coefficients of two spectra estimated for odd- and even-indexed half-sequences of the original time series. Phase values near zero are used as indicators of clutter contributions for Doppler bins close to zero velocity. Indicated Doppler bins are notched from the original spectrum, and the moments are then obtained. The identification scheme is motivated by spectra of an electronically steered phased array of the National Weather Radar Testbed (NWRT) and requires a sufficient number of pulses/samples for spectral analyses. However, the method can be used with a mechanically steered antenna with an appropriate adjustment compensating smearing due to antenna rotation. The method was tested on several NWRT data sets obtained in clear air and in precipitation. One example of clutter-filtered power in precipitation is shown here. There is no baseline for comparison, as the NWRT does not have clutter filtering at the present time. Nonetheless, for a comparison of power- and phase-based identification schemes, a power-based clutter filter similar to the one used by the National Weather Service on the network of mechanically steerable Weather Surveillance Doppler radars WSR-88Ds is implemented on NWRT and used as a preliminary baseline for comparison.

Adaptive clutter filtering based on sparse component analysis in ultrasound color flow imaging

An adaptive method based on the sparse component analysis is proposed for stronger clutter filtering in ultrasound color flow imaging (CFI). In the present method, the focal underdetermined system solver (FOCUSS) algorithm is employed, and the iteration of the algorithm is based on weighted norm minimization of the dependent variable with the weights being a function of the preceding iterative solutions. By finding the localized energy solution vector representing strong clutter components, the FOCUSS algorithm first extracts the clutter from the original signal. However, the different initialization of the basis function matrix has an impact on the filtering performance of FOCUSS algorithms. Thus, 2 FOCUSS clutter- filtering methods, the original and the modified, are obtained by initializing the basis function matrix using a predetermined set of monotone sinusoids and using the discrete Karhunen-Loeve transform (DKLT) and spatial averaging, respectively. Validation of 2 FOCUSS filtering methods has been performed through experimental tests, in which they were compared with several conventional clutter filters using simplistic simulated and gathered clinical data. The results demonstrate that 2 FOCUSS filtering methods can follow signal varying adaptively and perform clutter filtering effectively. Moreover, the modified method may obtain the further improved filtering performance and retain more blood flow information in regions close to vessel walls.

Adaptive Clutter Rejection for 3D Color Doppler Imaging: Preliminary Clinical Study

In three-dimensional (3D) ultrasound color Doppler imaging (CDI), effective rejection of flash artifacts caused by tissue motion (clutter) is important for improving sensitivity in visualizing blood flow in vessels. Since clutter characteristics can vary significantly during volume acquisition, a clutter rejection technique that can adapt to the underlying clutter conditions is desirable for 3D CDI. We have previously developed an adaptive clutter rejection (ACR) method, in which an optimum filter is dynamically selected from a set of predesigned clutter filters based on the measured clutter characteristics. In this article, we evaluated the ACR method with 3D in vivo data acquired from 37 kidney transplant patients clinically indicated for a duplex ultrasound examination. We compared ACR against a conventional clutter rejection method, down-mixing (DM), using a commonly-used flow signal-to-clutter ratio (SCR) and a new metric called fractional residual clutter area (FRCA). The ACR method was more effective in removing the flash artifacts while providing higher sensitivity in detecting blood flow in the arcuate arteries and veins in the parenchyma of transplanted kidneys. ACR provided 3.4 dB improvement in SCR over the DM method (11.4 ± 1.6 dB versus 8.0 ± 2.0 dB, p < 0.001) and had lower average FRCA values compared with the DM method (0.006 ± 0.003 versus 0.036 ± 0.022, p < 0.001) for all study subjects. These results indicate that the new ACR method is useful for removing nonstationary tissue motion while improving the image quality for visualizing 3D vascular structure in 3D CDI. (E-mail: ykim@u.washington.edu)

High-Frequency Subharmonic Pulsed-Wave Doppler and Color Flow Imaging of Microbubble Contrast Agents

A recent study has shown the feasibility of subharmonic (SH) flow imaging at a transmit frequency of 20 MHz. This paper builds on these results by examining the performance of SH flow imaging as a function of transmit pressure. Further, we also investigate the feasibility of SH pulsed-wave Doppler (PWD) imaging. In vitro flow experiments were performed with a 1-mm-diameter wall-less vessel cryogel phantom using the ultrasound contrast agent Definity™ and an imaging frequency of 20 MHz. The phantom results show that there is an identifiable pressure range where accurate flow velocity and power estimates can be made with SH imaging at 10 MHz (SH10), above which velocity estimates are biased by radiation force effects and unstable bubble behavior, and below which velocity and power estimates are degraded by poor SNR. In vivo validation of SH PWD was performed in an arteriole of a rabbit ear, and blood velocity estimates compared well with fundamental (F20) mode PWD. The ability to suppress tissue signals using SH signals may enable the use of higher frame rates and improve sensitivity to microvascular flow or slow velocities near large vessel walls by reducing or eliminating the need for clutter filters. ( E-mail: aneedles@visualsonics.com)

A Parametric Time Domain Method for Spectral Moment Estimation and Clutter Mitigation for Weather Radars

Abstract A parametric time domain method (PTDM) for clutter mitigation and precipitation spectral moments’ estimation for weather radars is introduced. Use of PTDM allows for the simultaneous estimation of clutter and precipitation echo spectral moments. It is shown that this approach leads to accurate estimates of precipitation spectral moments in the presence of clutter. Based on simulations, the PTDM performance is evaluated and compared against the clutter spectral filtering technique. In this study special attention is paid to the cases of strong clutter contamination. Furthermore, both methods, the PTDM and spectral clutter filter, are illustrated using the Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) observations.

Novel data association schemes for the probability hypothesis density filter

The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multi-target Alter based on finite set statistics. It propagates the PHD function, a first-order moment of the full multi-target posterior density. The peaks of the PHD function give estimates of target states. However, the PHD filter keeps no record of target identities and hence does not produce track-valued estimates of individual targets. We propose two different schemes according to which PHD filter can provide track-valued estimates of individual targets. Both schemes use the probabilistic data-association functionality albeit in different ways. In the first scheme, the outputs of the PHD filter are partitioned into tracks by performing track-to-estimate association. The second scheme uses the PHD filter as a clutter filter to eliminate some of the clutter from the measurement set before it is subjected to existing data association techniques. In both schemes, the PHD filter effectively reduces the size of the data that would be subject to data association. We consider the use of multiple hypothesis tracking (MHT) for the purpose of data association. The performance of the proposed schemes are discussed and compared with that of MHT.

Optimal velocity estimation in ultrasound color flow imaging in presence of clutter

In color flow imaging (CFI), the rejection of tissue clutter signal is treated separately from blood velocity estimation by high-pass filtering the received Doppler signal. The complete suppression of clutter is then difficult to achieve without affecting the subsequent velocity estimates. In this work, a different approach to velocity estimation is investigated, based on a statistical model of the signal from both clutter and blood. An analytic expression for the Cramer-Rao lower bound (CRLB) is developed, and used to determine the existence of an efficient maximum likelihood estimator (MLE) of blood velocity in CFI when assuming full knowledge of the clutter statistics. We further simulate and compare the performance of the MLE to that of the autocorrelation method (ACM) using finite-impulse response (FIR) and polynomial regression clutter filters. Two signal scenarios are simulated, representing a central and peripheral vessel. Simulations showed that, by including 3-9 (independent) spatial points, the MLE variance approached the CRLB in both scenarios. The ACM was approximately unbiased only for the central scenario in the clutter filter pass band, then with a variance of up to four times the CRLB. The ACM suffered from a severe bias in the filter transition region, and a significant performance gain was achieved here using the MLE. For practical use, the clutter properties must be estimated. We finally replaced the known clutter statistics with an estimate obtained from low-rank approximations of the received sample correlation matrix. Used in the model-based framework, this method came close to the performance of the MLE, and it may be an important step toward a practical model-based estimator, including tissue clutter with optimal performance.

Contrast imaging with chirped excitation

Coded excitation has been successfully used in imaging to increase the signal-to-noise ratio (SNR) and penetration depth. With a contrast agent, wideband signals have been hypothesized to increase the contrast-to-tissue ratio (CTR). However, nonlinear properties of contrast agents make decoding difficult when applying coded excitation to contrast imaging. We propose two chirped excitation methods to image contrast agents, with a mechanical index (MI) ranging from 0.05 to 0.34. In the single chirp method, one chirp is transmitted, followed by a clutter filter to reject tissue echoes, then a matched filter is used to recover range resolution. In the chirp sequence method, an increasing and decreasing chirp sequence is transmitted followed by subtraction of the compressed echoes to reject tissue echoes (assuming tissue is a linear scatterer at low MI). Ten independent acoustic experiments were performed to evaluate the CTR for chirp and tone burst insonation, with the same spatial peak temporal averaged intensity (I(SPTA)). A significant increase in CTR, ranging from 4 dB to 8 dB, is observed for chirped excitation as compared with tone burst insonation, at an I(SPTA) of 0.1 and 0.3 mW/cm2 (P < or = 5e-3). To achieve the same CTR of 15 dB, the spatial peak pulse averaged intensity (I(SPPA)) can be decreased by 6 dB for chirp insonation as compared with tone burst insonation (P < 1e-5). Additionally, an increase of more than 10 dB in tissue rejection ratio (TRR) is observed for a chirp sequence insonation compared to tone burst phase inversion for this set of parameters (P < or = 1e-9). Deconvolution of the linear microbubble response from the received echoes is proposed as a method to recover spatial resolution. The difference in the axial resolution resulting from chirp and three-cycle tone burst insonation is approximately 220 microm. The difference in the mainlobe width between experimental and predicted compressed echoes is less than 20%. The side-lobe amplitude is 9 dB to 16 dB below the mainlobe with a transmitted I(SPTA) from 0.1 to 6.6 mW/cm2.

Real-time adaptive clutter rejection filtering in color flow imaging using power method iterations

We propose a new algorithm for real-time, adaptive-clutter-rejection filtering in ultrasound color flow imaging (CFI) and related techniques. The algorithm is based on regression filtering using eigenvectors of the signal correlation matrix as a basis for representing clutter, a method that previously has been considered too computationally demanding for real-time processing in general CFI applications. The data acquisition and processing scheme introduced allows for a more localized sampling of the clutter statistics and, therefore, an improved clutter attenuation for lower filter orders. By using the iterative power method technique, the dominant eigenvalues and corresponding eigenvectors of the correlation matrix can be estimated efficiently, rendering real-time operation feasible on desktop computers. A new adaptive filter order algorithm is proposed that successfully estimates the proper dimension of the clutter basis, previously one of the major drawbacks of this clutter-rejection technique. The filter algorithm performance and computational demands has been compared to that of conventional clutter filters. Examples have been included which confirms that, by adapting the clutter-rejection filter to estimates of the clutter-signal statistics, improved attenuation of the clutter signal can be achieved in normal as well as more excessive cases of tissue movement and acceleration.

Ground Clutter Filtering Dual-Polarized, Staggered PRT Sequences

Abstract A procedure to filter the ground clutter from a dual-polarized, staggered pulse repetition time (PRT) sequence and recover the complex spectral coefficients of the weather signal is presented. While magnitude spectra are sufficient for estimation of the spectral moments from staggered PRT sequences, computation of differential phase in dual-polarized radars requires recovery of the complex spectra. Herein a method is given to recover the complex spectral coefficients after the ground clutter is filtered. Under the condition of “narrow” spectra, it is possible to recover the differential phase, ΦDP, and the copolar correlation coefficient, ρhv, accurately, in addition to the differential reflectivity, ZDR. The technique is tested on simulated time series and on actual radar data. The efficacy of the method is demonstrated on plan position indicator (PPI) plots of polarimetric variables.

Performance evaluation of eigendecomposition-based adaptive clutter filter for color flow imaging

In this paper, a newly developed eigendecomposition-based adaptive filter is applied in flow velocity estimation and its performance is evaluated and compared with the static polynomial regression filter. Results demonstrate that the eigendecomposition-based filter is the fully-adaptive and can not only suppress the clutter signals with minimal loss of low-flow signal, but also give less influence on the flow velocity estimation especially under the circumstance of high clutter velocity.

Ultrasound clutter filtering with iterative high pass filter selection

A system and method for ultrasound clutter filtering is provided. A processor is configured to iteratively select an optimal high pass filter for the progressive, ordered filtering of clutter from ultrasound color flow imaging data. The high pass filter input for each iterative selection and ordered set of high pass filters is the same original ultrasound color flow imaging data. The high pass filters have different cutoff frequencies whereby each high pass filter can be implemented using different structures. The system and method allow for filtering of clutter from ultrasound color flow imaging data until the clutter is substantially removed.