Clutter Filtering Research Articles

Mechanical wave velocities by clutter filter wave imaging detects myocardial dysfunction in acute coronary syndrome

Abstract Background/introduction Mechanical waves within the myocardium arising from physiologic events in the cardiac cycle such as the atrial kick (AK), mitral valve closure (MVC) and aortic valve closure (AVC) offer insight to tissue stiffness and can be measured by high frame rate echocardiography. Purpose To investigate the feasibility of measuring mechanical wave velocities in the left ventricle by clutter filter wave imaging (CFWI) and its potential for detecting regional myocardial dysfunction in patients with acute coronary syndrome. Methods We examined 60 patients (66±9 years of age, 72% male) with acute coronary syndrome by high frame rate echocardiography after revascularisation, imaging the left ventricle from the parasternal long axis view and three apical projections. Intrinsic mechanical waves generated by the AK, MVC and AVC were analysed off-line by CFWI of all walls using custom software (PyMWI) on 2000 frames of in-phase and quadrature component data per view extracted from a Vivid E95 scanner. Results We analysed a total number of 2372 waves recorded with a frame rate of 1180 (1134-1199) frames per second. The feasibility for measuring AK waves was high in all regions (mean 89%), but lower for the MVC and AVC waves with significant variability between regions (mean 44% and 46%, respectively; p<0.001). Feasibility declined from basal to apical segments for all waves (p=0.0001). Mean mechanical wave velocity was slowest for the AK wave at 2.5 (2.0-3.0) m/s, faster for the AVC wave at 5.1 (3.5-6.3) m/s and fastest for the MVC wave at 6.6 (4.9-8.6) m/s (p=0.0001). Basal AK wave velocity was faster in patients with basal wall motion abnormalities compared to those without (2.7 vs. 2.0 m/s, p=0.0004) and could detect the presence of wall motion abnormalities adjusting for age, gender and parameters of diastolic function (univariable OR 3.4, 95% CI 1.5-7.7, p<0.01; multivariable OR 4.4, 95% CI 1.4-14.1, p=0.01). Basal AK wave velocity detected wall motion abnormalities with an AUC of 0.78 with cut-off speeds of 2.3 m/s and 3.0 m/s, both correctly classifying 73% of cases (sensitivity/specificity 81%/68% and 43%/89%, respectively). Mechanical wave velocities of other waves and in other regions were not consistently and only weakly associated with echocardiographic parameters of systolic and diastolic function. Conclusion(s) Mechanical wave velocities by CFWI detects myocardial dysfunction in acute coronary syndrome. Elevated basal AK wave velocity was independently associated with myocardial dysfunction where AK wave velocity ≥2.3 m/s provided a good sensitivity and ≥3.0 m/s a good specificity for detecting regional wall motion abnormalities. The feasibility of measuring the AK wave was good, whereas MVC and AVC waves had lower feasibility and were inadequate for assessment of myocardial function. Feasibility variations between AK and MVC/AVC waves suggest differences in wave propagation dynamics or intrinsic wave properties.Illustration Clutter Filter Wave ImagingRisk of myocardial dysfunction

A New Paradigm for Automated Ground Clutter Removal: Global Regression Filtering

Abstract Ground clutter filtering is an important and necessary step for quality control of ground-based weather radars and this is widely done in the spectral domain via use of the Discrete Fourier Transform (DFT). To accomplish spectral clutter filtering, the I (in-phase) and Q (quadrature) time series are multiplied by a window function, such as the Blackman window, which suppresses clutter leakage. Subsequently, the clutter is filtered from the Doppler spectrum by setting spectral coefficients to zero where there is clutter signal. Recently, it has been shown with simulations that a regression clutter filter is a viable alternative. To demonstrate the regression filter, it is applied to radar data from both National Science Foundation-National Center for Atmospheric Research (NSF-NCAR) S-band polarimetric radar (S-Pol), and NEXRAD (Next Generation Weather Radar). A regression filter can be applied directly to the non-windowed time series data, thus avoiding the signal attenuation from a window function, which is required for spectral domain clutter filters such as GMAP (Gaussian Model Adaptive Processing). It is shown that the regression filter rejects clutter as effectively as the spectral technique, or better, but has the distinct advantage that standard error of estimates of the radar variables are in general improved by about 25% to 50% over a comparable spectral domain filter. The regression filter is straightforward and can be executed in real time.

Dynamic mode decomposition based Doppler monitoring of de novo cavitation induced by pulsed HIFU: an in vivo feasibility study

Pulsed high-intensity focused ultrasound (pHIFU) has the capability to induce de novo cavitation bubbles, offering potential applications for enhancing drug delivery and modulating tissue microenvironments. However, imaging and monitoring these cavitation bubbles during the treatment presents a challenge due to their transient nature immediately following pHIFU pulses. A planewave bubble Doppler technique demonstrated its potential, yet this Doppler technique used conventional clutter filter that was originally designed for blood flow imaging. Our recent study introduced a new approach employing dynamic mode decomposition (DMD) to address this in an ex vivo setting. This study demonstrates the feasibility of the application of DMD for in vivo Doppler monitoring of the cavitation bubbles in porcine liver and identifies the candidate monitoring metrics for pHIFU treatment. We propose a fully automated bubble mode identification method using k-means clustering and an image contrast-based algorithm, leading to the generation of DMD-filtered bubble images and corresponding Doppler power maps after each HIFU pulse. These power Doppler maps are then correlated with the extent of tissue damage determined by histological analysis. The results indicate that DMD-enhanced power Doppler map can effectively visualize the bubble distribution with high contrast, and the Doppler power level correlates with the severity of tissue damage by cavitation. Further, the temporal characteristics of the bubble modes, specifically the decay rates derived from DMD, provide information of the bubble dissolution rate, which are correlated with tissue damage level—slower rates imply more severe tissue damage.

High-Resolution Power Doppler Using Null Subtraction Imaging.

To improve the spatial resolution of power Doppler (PD) imaging, we explored null subtraction imaging (NSI) as an alternative beamforming technique to delay-and-sum (DAS). NSI is a nonlinear beamforming approach that uses three different apodizations on receive and incoherently sums the beamformed envelopes. NSI uses a null in the beam pattern to improve the lateral resolution, which we apply here for improving PD spatial resolution both with and without contrast microbubbles. In this study, we used NSI with three types of singular value decomposition (SVD)-based clutter filters and noise equalization to generate high-resolution PD images. An element sensitivity correction scheme was also proposed as a crucial component of NSI-based PD imaging. First, a microbubble trace experiment was performed to evaluate the resolution improvement of NSI-based PD over traditional DAS-based PD. Then, both contrast-enhanced and contrast free ultrasound PD images were generated from the scan of a rat brain. The cross-sectional profile of the microbubble traces and microvessels were plotted. FWHM was also estimated to provide a quantitative metric. Furthermore, iso-frequency curves were calculated to provide a resolution evaluation metric over the global field of view. Up to six-fold resolution improvement was demonstrated by the FWHM estimate and four-fold resolution improvement was demonstrated by the iso-frequency curve from the NSI-based PD microvessel images compared to microvessel images generated by traditional DAS-based beamforming. A resolvability of [Formula: see text] was measured from the NSI-based PD microvessel image. The computational cost of NSI-based PD was only increased by 40 percent over the DAS-based PD.

A Study of Mechanical Scanning High-frequency Ultrasonic Color Flow Imaging

Abstract High-frequency Ultrasonic imaging can promote high-resolution images and detailed diagnostic information, which has great clinical application prospects. However, due to the difficulty and high cost of high-frequency multi-channel ultrasound systems and multi-array element transducers, it is not conducive to popularization. Mechanical scanning high-frequency ultrasound imaging system is an alternative and low-cost method. However, current mechanical scanning systems cannot perform ultrasound flow imaging, and hemodynamic information is clinically important. Therefore, an improved high-frequency, miniaturized mechanical scanning system for color flow imaging was designed and developed. The system uses a crank linkage structure to drive a miniature transducer with a center frequency of 12 MHz for reciprocating scanning imaging within a range of 15 mm. By optimizing the clutter filter and dynamic domain flow imaging process, two methods are realized: real-time rapid ultrasound imaging and highly sensitive ultrasound flow imaging. The two optimized methods used power Doppler and color flow Doppler for flow phantom imaging, and the final measured blood flow error was less than 5%, which indicates that the mechanical scanning high-frequency ultrasonic color flow imaging system has a good application prospect.

Research on prediction of water target motion status of unmanned surface vehicle based on lidar

To improve the perception ability of unmanned boats on water targets and improve the autonomous collision avoidance function of unmanned boats, the problem of target motion state prediction is studied and a method for predicting the motion state of unmanned water targets based on lidar is proposed. Firstly, a three-dimensional point cloud space model is established and the down-sampling and clutter filtering are completed through various filters. Secondly, based on European clustering target segmentation, a target tracking algorithm combined with 3D-IOU metric is proposed to achieve target tracking. Finally, the Kalman filter is introduced for denoising, combined with the iterative closest point algorithm (Iterative Closest Point, ICP) and the centroid calculation algorithm to realize the prediction of the target speed and course. After the test on the lake, the method can meet the real-time requirements, complete the accurate prediction of the target motion state and control the error at a low level. At the same time, this method can also lay the foundation for the development of subsequent autonomous navigation tasks of unmanned vehicles.

Competitive Swarm Optimized SVD Clutter Filtering for Ultrafast Power Doppler Imaging.

Ultrafast power Doppler imaging (uPDI) can significantly increase the sensitivity of resolving small vascular paths in ultrasound. While clutter filtering is a fundamental and essential method to realize uPDI, it commonly uses singular value decomposition (SVD) to suppress clutter signals and noise. However, current SVD-based clutter filters using two cutoffs cannot ensure sufficient separation of tissue, blood, and noise in uPDI. This article proposes a new competitive swarm-optimized SVD clutter filter to improve the quality of uPDI. Specifically, without using two cutoffs, such a new filter introduces competitive swarm optimization (CSO) to search for the counterparts of blood signals in each singular value. We validate the CSO-SVD clutter filter on public in vivo datasets. The experimental results demonstrate that our method can achieve higher contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and blood-to-clutter ratio (BCR) than the state-of-the-art SVD-based clutter filters, showing a better balance between suppressing clutter signals and preserving blood signals. Particularly, our CSO-SVD clutter filter improves CNR by 0.99 ± 0.08 dB, SNR by 0.79 ± 0.08 dB, and BCR by 1.95 ± 0.03 dB when comparing a spatial-similarity-based SVD clutter filter in the in vivo dataset of rat brain bolus.

Adaptive higher-order singular value decomposition clutter filter for ultrafast Doppler imaging of coronary flow under non-negligible tissue motion

Background and Objective:With the development of advanced clutter-filtering techniques by singular value decomposition (SVD) and leveraging favorable acquisition settings such as open-chest imaging by a linear high-frequency probe and plane waves, several studies have shown the feasibility of cardiac flow measurements during the entire cardiac cycle, ranging from coronary flow to myocardial perfusion. When applying these techniques in a routine clinical setting, using transthoracic ultrasound imaging, new challenges emerge. Firstly, a smaller aperture is needed that can fit between ribs. Consequently, diverging waves are employed instead of plane waves to achieve an adequate field of view. Secondly, to ensure imaging at a larger depth, the maximum pulse repetition frequency has to be reduced. Lastly, in comparison to the open-chest scenario, tissue motion induced by the heartbeat is significantly stronger. The latter complicates substantially the distinction between clutter and blood signals. Methods:This study investigates a strategy to overcome these challenges by diverging wave imaging with an optimal number of tilt angles, in combination with dedicated clutter-filtering techniques. In particular, a novel, adaptive, higher-order SVD (HOSVD) clutter filter, which utilizes spatial, temporal, and angular information of the received ultrasound signals, is proposed to enhance clutter and blood separation. Results:When non-negligible tissue motion is present, using fewer tilt angles not only reduces the decorrelation between the received waveforms but also allows for collecting more temporal samples at a given ensemble duration, contributing to improved Doppler performance. The addition of a third angular dimension enables the application of HOSVD, providing greater flexibility in selecting blood separation thresholds from a 3-D tensor. This differs from the conventional threshold selection method in a 2-D spatiotemporal space using SVD. Exhaustive threshold search has shown a significant improvement in Contrast and Contrast-to-Noise ratio for Power Doppler images filtered with HOSVD compared to the SVD-based clutter filter. Conclusion:With the improved settings, the obtained Power Doppler images show the feasibility of measuring coronary flow under the influence of non-negligible tissue motion in both in vitro and ex vivo.

Detection of Individual Microbubbles by Burst-Wave-Aided Contrast-Enhanced Active Doppler Ultrasonography.

We propose burst-wave-aided, contrast-enhanced, active Doppler ultrasonography for visualizing lymph vessels. This technique forces ultrasound contrast agents (UCAs) to move using the acoustic radiation force induced by burst waves with low amplitude while suppressing their destruction. Using a flow phantom, we measured the average, decrease rate of echo intensity [i.e., pulse intensity integral (PII)], and the velocity of individual contrast agents, which directly affects the performance of imaging and tracking contrast agents under stationary flow conditions. Comparison with pulse-inversion Doppler without exposure to the burst wave demonstrated that the velocity of the contrast agents could be enhanced up to several tens of millimeters per second by the effect of the burst wave, maximizing the echo intensity extracted by a clutter filter. The contrast ratio (CR), defined as the ratio of the contrast echo to the phantom echo outside the channel, did not change appreciably, even when the lower cut-off velocity of the clutter filter was increased up to 10 mm/s. This implies a better robustness against the motion of the tissue. In addition, the performance for detecting contrast agents (i.e., echo intensity) was superior or similar to that of pulse-inversion Doppler, even in undesirable conditions where the flow had a velocity component in the opposite direction to that of the acoustic radiation force. The echo intensity was lower or the same as that in pulse-inversion Doppler, demonstrating the potential for suppressing the destruction of contrast agents and enabling long-term observations. From these results, we expect that the proposed method will be beneficial for visualizing lymph vessels.

Computationally Efficient SVD Filtering for Ultrasound Flow Imaging and Real-Time Application to Ultrafast Doppler.

Over the past decade, ultrasound microvasculature imaging has seen the rise of highly sensitive techniques, such as ultrafast power Doppler (UPD) and ultrasound localization microscopy (ULM). The cornerstone of these techniques is the acquisition of a large number of frames based on unfocused wave transmission, enabling the use of singular value decomposition (SVD) as a powerful clutter filter to separate microvessels from surrounding tissue. Unfortunately, SVD is computationally expensive, hampering its use in real-time UPD imaging and weighing down the ULM processing chain, with evident impact in a clinical context. To solve this problem, we propose a new approach to implement SVD filtering, based on simplified and elementary operations that can be optimally parallelized on GPU (GPU sSVD), unlike standard SVD algorithms that are mainly serial. First, we show that GPU sSVD filters UPD and ULM data with high computational efficiency compared to standard SVD implementations, and without losing image quality. Second, we demonstrate that the proposed method is suitable for real-time operation. GPU sSVD was embedded in a research scanner, along with the spatial similarity matrix (SSM), a well-known efficient approach to automate the selection of SVD blood components. High real-time throughput of GPU sSVD is demonstrated when using large packets of frames, with and without SSM. For example, more than 15000 frames/s were filtered with 512 packet size on a 128 × 64 samples beamforming grid. Finally, GPU sSVD was used to perform, for the first time, UPD imaging with real-time and adaptive SVD filtering on healthy volunteers.

Mechanical Wave Velocities in Left Ventricular Walls in Healthy Subjects and Patients With Aortic Stenosis

BackgroundMechanical wave velocity (MWV) measurement is a promising method for evaluating myocardial stiffness, because these velocities are higher in patients with myocardial disease. ObjectivesUsing high frame rate echocardiography and a novel method for detection of myocardial mechanical waves, this study aimed to estimate the MWVs for different left ventricular walls and events in healthy subjects and patients with aortic stenosis (AS). Feasibility and reproducibility were evaluated. MethodsThis study included 63 healthy subjects and 13 patients with severe AS. All participants underwent echocardiographic examination including 2-dimensional high frame rate recordings using a clinical scanner. Cardiac magnetic resonance was performed in 42 subjects. The authors estimated the MWVs at atrial kick and aortic valve closure in different left ventricular walls using the clutter filter wave imaging method. ResultsMechanical wave imaging in healthy subjects demonstrated the highest feasibility for the atrial kick wave reaching >93% for all 4 examined left ventricular walls. The MWVs were higher for the inferolateral and anterolateral walls (2.2 and 2.6 m/s) compared with inferoseptal and anteroseptal walls (1.3 and 1.6 m/s) (P < 0.05) among healthy subjects. The septal MWVs at aortic valve closure were significantly higher for patients with severe AS than for healthy subjects. ConclusionsMWV estimation during atrial kick is feasible and demonstrates higher velocities in the lateral walls, compared with septal walls. The authors propose indicators for quality assessment of the mechanical wave slope as an aid for achieving consistent measurements. The discrimination between healthy subjects and patients with AS was best for the aortic valve closure mechanical waves. (Ultrasonic Markers for Myocardial Fibrosis and Prognosis in Aortic Stenosis; NCT03422770)

Fast thresholding of SVD clutter filter using the spatial similarity matrix and a sum-table algorithm.

Singular value decomposition (SVD) has become a standard for clutter filtering of ultrafast ultrasound datasets. Its implementation requires the choice of appropriate thresholds to discriminate the singular value subspaces associated to tissue, blood and noise signals. Comparing the similarity of the spatial singular vectors was shown to be a robust and efficient method to estimate the SVD thresholds. The correlation of the spatial singular vectors envelopes gives the spatial similarity matrix (SSM), which usually exhibits two square-like domains juxtaposed along the diagonal of the SSM, representing the tissue and the blood subspaces. Up to now, the proposed methods to automatically segment these two subspaces on the SSM were of high computational complexity and had long processing time. Here we propose an optimized algorithm using a sum-table approach that decreases the complexity by two orders of magnitude: O(n4) to O(n2). The proposed method resulted in processing times lower than 0.08s for datasets of 2000 frames, whereas previous algorithms took more than 26 hours, so an improvement by a factor 106. We illustrated this adaptive square-fitting on the SSM in the in VIVO case of human neonate brain imaging and carotid imaging with various conditions of clutter. This optimization of SVD thresholding is essential to develop the use of adaptive clutter filtering, especially for real-time applications or block-wise processing.

Clinical Application of a New Ultrasound Technique: Microvascular Imaging of the Liver

Microvascular imaging is a new ultrasound technique aiming to image and quantify the microvasculature, comprising arterioles, venules, and maybe even capillaries. The technique uses an advanced clutter filter to eliminate artifacts caused by breathing and movement and retains the low-speed blood signals in microvessels. The great advantage of microvascular imaging in ultrasonography is that it allows a physician intuitively to detect very slow blood signals in microvessels, providing additional information about flow distribution in the target area without the use of contrast media. This technical advance in ultrasonography will improve diagnosis in various clinical situations. In this review, we focus on microvascular imaging in the liver.

Effect of Clutter Filter in High-Frame-Rate Ultrasonic Backscatter Coefficient Analysis.

High-frame-rate imaging with a clutter filter can clearly visualize blood flow signals and provide more efficient discrimination with tissue signals. In vitro studies using clutter-less phantom and high-frequency ultrasound suggested a possibility of evaluating the red blood cell (RBC) aggregation by analyzing the frequency dependence of the backscatter coefficient (BSC). However, in in vivo applications, clutter filtering is required to visualize echoes from the RBC. This study initially evaluated the effect of the clutter filter for ultrasonic BSC analysis for in vitro and preliminary in vivo data to characterize hemorheology. Coherently compounded plane wave imaging at a frame rate of 2 kHz was carried out in high-frame-rate imaging. Two samples of RBCs suspended by saline and autologous plasma for in vitro data were circulated in two types of flow phantoms without or with clutter signals. The singular value decomposition was applied to suppress the clutter signal in the flow phantom. The BSC was calculated using the reference phantom method, and it was parametrized by spectral slope and mid-band fit (MBF) between 4-12 MHz. The velocity distribution was estimated by the block matching method, and the shear rate was estimated by the least squares approximation of the slope near the wall. Consequently, the spectral slope of the saline sample was always around four (Rayleigh scattering), independently of the shear rate, because the RBCs did not aggregate in the solution. Conversely, the spectral slope of the plasma sample was lower than four at low shear rates but approached four by increasing the shear rate, because the aggregations were presumably dissolved by the high shear rate. Moreover, the MBF of the plasma sample decreased from -36 to -49 dB in both flow phantoms with increasing shear rates, from approximately 10 to 100 s-1. The variation in the spectral slope and MBF in the saline sample was comparable to the results of in vivo cases in healthy human jugular veins when the tissue and blood flow signals could be separated.

Contrast analysis in ultrafast ultrasound blood flow imaging of jugular vein

PurposeThe contrasts of flowing blood in in vitro experiments using porcine blood and in vivo measurements of human jugular veins were analyzed to demonstrate that the hemorheological property was dependent on the shear rate.MethodsBlood samples (45% hematocrit) suspended in saline or plasma were compared with examine the difference in viscoelasticity. Ultrafast plane-wave imaging at an ultrasonic center frequency of 7.5 MHz was performed on different steady flows in a graphite-agar phantom. Also, in vivo measurement was performed in young, healthy subjects and patients with diabetes. A spatiotemporal matrix of beamformed radio-frequency data was used for the singular value decomposition (SVD) clutter filter. The clutter-filtered B-mode image was calculated as the amplitude envelope normalized at the first frame in the diastolic phase to evaluate contrast. The shear rate was estimated as the velocity gradient perpendicular to the lateral axis.ResultsAlthough nonaggregated erythrocytes at a high shear rate exhibited a low echogenicity, the echogenicity in the plasma sample overall increased due to erythrocyte aggregation at a low shear rate. In addition, the frequency of detection of specular components, defined as components beyond twice the standard deviation of a contrast map obtained from a clutter-filtered B-mode image, increased in the porcine blood at a high shear rate and the venous blood in healthy subjects versus patients with diabetes.ConclusionThe possibility of characterizing hemorheological properties dependent on the shear rate and diabetes condition was indicated using ultrafast plane-wave imaging with an SVD-based clutter filter.

Fast and Robust Clutter Filtering in Ultrafast Echocardiography.

Singular value decomposition (SVD)-based filters have become the norm for clutter filtering in ultrasound blood flow applications but are computationally expensive and susceptible to large and fast tissue motion. Randomized SVD (rSVD) has later been shown to successfully accelerate filtering of in vivo stationary tissues. However, little is known about its performance on ultrafast echocardiography, which produces thousands of frames to assess complex myocardial deformation and blood dynamics. Neither has its inherently robust randomized scheme been proven in any ultrasound blood flow imaging methods. This study thus proposed to employ rSVD as a fast and robust clutter filter for ultrafast echocardiograms prior to power Doppler analysis. Ultrafast echocardiograms of nine normal human hearts were acquired in vivo by our cascaded synthetic aperture imaging method. One subject was additionally scanned under four different sonographic signal-to-noise ratio (SNR) levels. Contrast ratio (CR) and contrast-to-noise ratio (CNR) of in vivo power Doppler images obtained from filtered ultrafast echocardiograms were calculated, and their mean and standard deviation within a cardiac cycle represented temporal average and variation of contrast resolution, respectively. Our in vivo results showed that rSVD accelerated clutter filtering by 12-fold and provided significantly better local contrast (mean CNR values: p < 0.001) while being equally effective (mean CR values: p=0.20) compared with full-SVD. rSVD yielded smaller standard deviations of CR (1.32 dB vs. 5.49 dB) and CNR (1.27 dB vs. 5.49 dB) than full-SVD in the lowest SNR scenario, thus substantiating its superior robustness. Our findings suggest using rSVD in ultrafast echocardiographic blood dynamics analysis.

Adaptive noise reduction for power Doppler imaging using SVD filtering in the channel domain and coherence weighting of pixels

Objective. Ultrafast power Doppler (UPD) is an ultrasound method that can image blood flow at several thousands of frames per second. In particular, the high number of data provided by UPD enables the use of singular value decomposition (SVD) as a clutter filter for suppressing tissue signal. Notably, is has been demonstrated in various applications that SVD filtering increases significantly the sensitivity of UPD to microvascular flows. However, UPD is subjected to significant depth-dependent electronic noise and an optimal denoising approach is still being sought. Approach. In this study, we propose a new denoising method for UPD imaging: the Coherence Factor Mask (CFM). This filter is first based on filtering the ultrasound time-delayed data using SVD in the channel domain to remove clutter signal. Then, a spatiotemporal coherence mask that exploits coherence information between channels for identifying noisy pixels is computed. The mask is finally applied to beamformed images to decrease electronic noise before forming the power Doppler image. We describe theoretically how to filter channel data using a single SVD. Then, we evaluate the efficiency of the CFM filter for denoising in vitro and in vivo images and compare its performances with standard UPD and with three existing denoising approaches. Main results. The CFM filter gives gains in signal-to-noise ratio and contrast-to-noise ratio of up to 22 dB and 20 dB, respectively, compared to standard UPD and globally outperforms existing methods for reducing electronic noise. Furthermore, the CFM filter has the advantage over existing approaches of being adaptive and highly efficient while not requiring a cut-off for discriminating noise and blood signals nor for determining an optimal coherence lag. Significance. The CFM filter has the potential to help establish UPD as a powerful modality for imaging microvascular flows.

The Impacts of Terrestrial Wind Turbine’s Operation on Telecommunication Services

This paper presents a compendious review for the evaluation and description of the mathematical modelling of the affected components in wind turbines which cause the scattering of communication signals. The impact of an adjacent wind farm operation on telecommunication signals is that it induces electromagnetic interference (EMI) in radar, television and radio signals, resulting from the complex rotating blade’s geometry of the wind turbines. Thus, altering the quality of the reflected signal, especially the capability of the radar detection. In all the modelling studies, the radar cross section (RCS) model of a wind turbine’s blade is found to be the most complex, due to its huge computational burden. However, clutter filtering is another interesting technique, which employs the Doppler signal processing to obviate the huge computational task in RCS. In this case, the rotating blades of the wind turbine produce Doppler echoes, which in turn are used to estimate the model of the blade by modelling the echo of the scattering points. Therefore, this review succinctly compiles the basic steps of theoretical analysis and simulations of the impact of wind turbines on communication signals, and the remedies to minimize the impact.

Mm-DSF: A Method for Identifying Dangerous Driving Behaviors Based on the Lateral Fusion of Micro-Doppler Features Combined.

To address the dangerous driving behaviors prevalent among current car drivers, it is necessary to provide real-time, accurate warning and correction of driver's driving behaviors in a small, movable, and enclosed space. In this paper, we propose a method for detecting dangerous behaviors based on frequency-modulated continuous-wave radar (mm-DSF). The highly packaged millimeter-wave radar chip has good in-vehicle emotion recognition capability. The acquired millimeter-wave differential frequency signal is Fourier-transformed to obtain the intermediate frequency signal. The physiological decomposition of the local micro-Doppler feature spectrum of the target action is then used as the eigenvalue. Matrix signal intensity and clutter filtering are performed by analyzing the signal echo model of the input channel. The signal classification is based on the estimation and variety of the feature vectors of the target key actions using a modified and optimized level fusion method of the SlowFast dual-channel network. Nine typical risky driving behaviors were set up by the Dula Hazard Questionnaire and TEIQue-SF, and the accuracy of the classification results of the self-built dataset was analyzed to verify the high robustness of the method. The recognition accuracy of this method increased by 1.97% compared with the traditional method.

Genetic algorithm-based mathematical morphology for clutter removal in airborne radars

This paper presents a novel approach for clutter removal in airborne radars using a genetic algorithm and mathematical morphology. The clutter returns are detected when constant alarm rate processing is applied on range-Doppler images. In the proposed method, mathematical morphological operations are performed on range-Doppler images to obtain clutter images. The clutter image is then applied as a mask to remove false detections due to clutter. Also, the targets embedded in clutter are detected using gray-scale morphological operations. The morphological filter and the sequence of operations are designed by a genetic algorithm. The advantage of the proposed method is that it does not require the computation of statistical measures from clutter data and filters are optimally designed using a genetic algorithm. The proposed method has shown an increase in clutter leak reduction when compared to that of a deep morphological network.