The removal of wall components in Doppler ultrasound signals by using the empirical mode decomposition algorithm

Robust classification of heart valve sound based on adaptive EMD and feature fusion

Cardiovascular disease (CVD) is considered one of the leading causes of death worldwide. In recent years, this research area has attracted researchers' attention to investigate heart sounds to diagnose the disease. To effectively distinguish heart valve defects from normal heart sounds, adaptive empirical mode decomposition (EMD) and feature fusion techniques were used to analyze the classification of heart sounds. Based on the correlation coefficient and Root Mean Square Error (RMSE) method, adaptive EMD was proposed under the condition of screening the intrinsic mode function (IMF) components. Adaptive thresholds based on Hausdorff Distance were used to choose the IMF components used for reconstruction. The multidimensional features extracted from the reconstructed signal were ranked and selected. The features of waveform transformation, energy and heart sound signal can indicate the state of heart activity corresponding to various heart sounds. Here, a set of ordinary features were extracted from the time, frequency and nonlinear domains. To extract more compelling features and achieve better classification results, another four cardiac reserve time features were fused. The fusion features were sorted using six different feature selection algorithms. Three classifiers, random forest, decision tree, and K-nearest neighbor, were trained on open source and our databases. Compared to the previous work, our extensive experimental evaluations show that the proposed method can achieve the best results and have the highest accuracy of 99.3% (1.9% improvement in classification accuracy). The excellent results verified the robustness and effectiveness of the fusion features and proposed method.

Segmental Tissue Speckle Tracking Predicts the Stenosis Severity in Patients With Coronary Artery Disease

Objective

Two-dimensional speckle tracking echocardiography (2D-STE) has been used as a diagnostic tool for coronary artery disease (CAD). However, whether vessel supplied myocardial strain and strain rate (SR) predict the severity of coronary artery stenosis in patients with CAD is unknown. This study aimed to investigate correlation of cardiac mechanical parameters in tissue speckle tracking measurements with coronary artery stenosis diagnosed by cardiac catheterization in patients with clinically diagnosed CAD.

Methods and Results

Among 59 patients analyzed, 170 vessels were evaluated by coronary angiography and the corresponding echocardiography to quantify left ventricular myocardial strain and SR. The average longitudinal strain and SR of the segmental myocardium supplied by each coronary artery were calculated to achieve vessel myocardium strain (VMS) and strain rate (VMSR). The VMS and VMSR at each of four severity levels of stenosis showed significant differences among groups (p = 0.016, and p < 0.001, respectively). The strain and SR in vessels with very severe stenosis (≥75%, group IV; n = 29), 13.9 ± 4.3, and 0.9 ± 0.3, respectively, were significantly smaller than those of vessels with mild stenosis ≤ 25%, group I; n = 88, 16.9 ± 4.9, p = 0.023, and 1.2 ± 0.3, p = 0.001, respectively. The SR in vessels with moderate stenosis (26–49%, group II; n = 37), 1.0 ± 0.2, was significantly smaller than that in vessels with mild stenosis vessels (p = 0.021). The lower VMS and VMSR, the higher possibility of severe coronary stenosis is. The VMS and VMSR lower than 13.9 ± 4.3 and 0.9 ± 0.3, respectively predicted the severe coronary stenosis. The VMS and VMSR higher than 16.9 ± 4.9 and 1.2 ± 0.3, respectively predicted mild or no coronary artery stenosis.

Conclusions

The actual stenosis rate in catheterization demonstrates that this technique was able to assess coronary artery condition. Thus, the application of a non-invasive method of 2D-STE to evaluate and simplify diagnosis of CAD is feasible.

A dynamic ultrasound simulation of a pulsating three-layered CCA for validation of two-dimensional wall motion and blood velocity estimation algorithms

PURPOSE

A dynamic ultrasound simulation model for the common carotid artery (CCA) with three arterial layers for validation of two-dimensional wall motion and blood velocity estimation algorithms is proposed in the present study. This model describes layers with not only characteristics of echo distributions conforming to clinical ones but also varying thicknesses, axial, and radial displacements with pulsatile blood pressure during a cardiac cycle.

METHODS

The modeling process is as follows: first, a geometrical model according with the clinical structure size of a CCA is built based on the preset layer thicknesses and the diameter of lumen. Second, a three-dimensional scatterer model is constructed by a mapping with a Hilbert space-filling curve from the one-dimensional scatterer distribution with the position and amplitude following Gamma and Gaussian distributions, respectively. The characteristics of three layers and blood are depicted by smoothly adjusting the scatterer density, the scale, and shape parameters of the Gamma distribution as well as the mean and standard deviation of the Gaussian distribution. To obtain the values of parameters of scatterer distributions, including the shape parameter, density, and intensity, for arterial layers and blood, the envelope signals simulated from different configurations of scatterer distribution are compared with those from different kinds of tissue of CCAs in vivo through a statistic analysis. Finally, the dynamic scatterer model is realized based on the blood pressure, elasticity modulus of intima-media (IM) and adventitia, varying IM thickness, axial displacement of IM as well as blood flow velocity at central axis during a cardiac cycle. Then, the corresponding radiofrequency (RF) signals, envelope signals, and B-mode images of the pulsatile CCA are generated in a dynamic scanning mode using Field II platform.

RESULTS

The three arterial layers, blood, and surrounding tissue in simulated B-mode ultrasound images are clearly legible. The results based on a statistical analysis for the envelope signals from 30 simulations indicate that the echo characteristics of blood, intima, media, and adventitia are in accordant with clinical ones. The maximum relative errors between the preset geometrical sizes and the measured ones from the simulated images for the diameter of the lumen and the thicknesses of the intima, media, and adventitia are 0.13%, 3.89%, 1.35%, and 0.06%, respectively. For the dynamic parameters, the variation in IM thickness, the radial displacements of lumen and adventitia as well as the axial displacement of IM and blood flow velocity are measured with the mean relative errors of 68.03%, 9.27%, 2.10%, 4.93%, and 17.34%, respectively.

CONCLUSION

The simulated results present static sizes and dynamical variations according with preset values; echo distributions conforming to clinical versions. Therefore, the presented simulation model could be useful as a data source to evaluate the performance of studies on measurements of ultrasound-based tissue structures and dynamic parameters for the CCA layers.

Removal of BCG artefact from concurrent fMRI-EEG recordings based on EMD and PCA

BACKGROUND

Simultaneous electroencephalography (EEG) and functional magnetic resonance image (fMRI) acquisitions provide better insight into brain dynamics. Some artefacts due to simultaneous acquisition pose a threat to the quality of the data. One such problematic artefact is the ballistocardiogram (BCG) artefact.

METHODS

We developed a hybrid algorithm that combines features of empirical mode decomposition (EMD) with principal component analysis (PCA) to reduce the BCG artefact. The algorithm does not require extra electrocardiogram (ECG) or electrooculogram (EOG) recordings to extract the BCG artefact.

RESULTS

The method was tested with both simulated and real EEG data of 11 participants. From the simulated data, the similarity index between the extracted BCG and the simulated BCG showed the effectiveness of the proposed method in BCG removal. On the other hand, real data were recorded with two conditions, i.e. resting state (eyes closed dataset) and task influenced (event-related potentials (ERPs) dataset). Using qualitative (visual inspection) and quantitative (similarity index, improved normalized power spectrum (INPS) ratio, power spectrum, sample entropy (SE)) evaluation parameters, the assessment results showed that the proposed method can efficiently reduce the BCG artefact while preserving the neuronal signals.

COMPARISON WITH EXISTING METHODS

Compared with conventional methods, namely, average artefact subtraction (AAS), optimal basis set (OBS) and combined independent component analysis and principal component analysis (ICA-PCA), the statistical analyses of the results showed that the proposed method has better performance, and the differences were significant for all quantitative parameters except for the power and sample entropy.

CONCLUSIONS

The proposed method does not require any reference signal, prior information or assumption to extract the BCG artefact. It will be very useful in circumstances where the reference signal is not available.

Compound Doppler ultrasound signal simulation for pulsatile carotid arteries with a stenosis

BACKGROUND

The simulated Doppler blood flow signals are widely used to assess the performance of the clutter filters for removing wall components while reserving low-velocity signals scattered from physiological blood flow approaching the inner vessel-wall injured by a stenosed lesion.

OBJECTIVE

By simultaneously taking into account the natural attributes of the Doppler equipment, blood flow as well as vessel wall of pulsatile carotid arteries with a stenosis, a computer simulation method is presented to produce the compound Doppler ultrasound blood flow signals.

METHODS

The in-phase and quadrature (I/Q) axial as well as radial blood flow signals are simulated by superposing a series of cosine functions regulated by the spectrograms estimated from the axial and radial velocity profiles firstly obtained through the solution of the incompressible Navier-Stokes equations, respectively. Meanwhile, the I/Q Doppler signals echoed from pulsatile near (anterior) and far (posterior) walls are reproduced based on their radial movements during a cardiac cycle. Ultimately, those confirmed quadrature signals are summed to generate the compound Doppler signals including the contribution from both blood flow and stenosed vessel-wall.

RESULTS

The compound Doppler ultrasound signals echoed from both axial and radial blood flows as well as vessel walls with obstruction grades of 0% (normal arteries), 10% and 25% are simulated respectively. The real signals from the left carotid artery with an approximately 10% stenosis degree are also collected for further assessing the believability of simulated versions.

CONCLUSIONS

The simulated and clinical tests demonstrate that the proposed computer simulation method can produce compound Doppler signals with confirmed qualitative and quantitative characteristics resembled with the clinical versions, which could be used as an theoretical data source for evaluating the performance of the signal separation between pulsatile blood flows and vessel walls with mild stenosed-lesions.

Discrimination between newly formed and aged thrombi using empirical mode decomposition of ultrasound B-scan image

Ultrasound imaging is a first-line diagnostic method for screening the thrombus. During thrombus aging, the proportion of red blood cells (RBCs) in the thrombus decreases and therefore the signal intensity of B-scan can be used to detect the thrombus age. To avoid the effect of system gain on the measurements, this study proposed using the empirical mode decomposition (EMD) of ultrasound image as a strategy to classify newly formed and aged thrombi. Porcine blood samples were used for the in vitro induction of fresh and aged thrombi (at hematocrits of 40%). Each thrombus was imaged using an ultrasound scanner at different gains (15, 20, and 30 dB). Then, EMD of ultrasound signals was performed to obtain the first and second intrinsic mode functions (IMFs), which were further used to calculate the IMF-based echogenicity ratio (IER). The results showed that the performance of using signal amplitude of B-scan to reflect the thrombus age depends on gain. However, the IER is less affected by the gain in discriminating between fresh and aged thrombi. In the future, ultrasound B-scan combined with the EMD may be used to identify the thrombus age for the establishment of thrombolytic treatment planning.

Two-dimensional blood flow vectors obtained with bidirectional Doppler ultrasound

Precise measurement of blood flow is important because blood flow closely correlates formation of thrombus and atherosclerotic plaque. Among clinically applied modalities for blood flow measurement, color Doppler ultrasound shows two-dimensional (2D) distribution of one-dimensional blood flow component along the ultrasound beam. In the present study, 2D blood flow vector is obtained with high temporal and bidirectional Doppler ultrasound technique. Linear array probe with the central frequency of 7.5 MHz and an ultrasound data acquisition system with 128 transmit and 128 receive channels were equipped. Frame rate of 5 kHz was achieved by parallel receive beam forming with a wide transmitted wave. The flow velocity was measured from two different angles by beam steering. The interval of two measurements was 0.8 msec and it was considered as almost one moment to obtain 2D blood flow vector. B-mode image and 2D blood flow vector of the pulsatile flow in a carotid artery model showed small vortex at the bifurcation area. The method was also applied for visualization of in vivo blood flow vector in human carotid arteries. 2D blood flow measurement may predict the risk area of thrombus and plaque formation induced by abnormal blood flow.

Study of the effects of age and body mass index on the carotid wall vibration: Extraction methodology and analysis

This study aims to non-invasively extract the vibrations of the carotid wall and evaluate the changes in the carotid artery wall caused by age and obesity. Such evaluation can increase the possibility of detecting wall stiffness and atherosclerosis in its early stage. In this study, a novel method that uses a phase-tracking method based on the continuous wavelet transform calculates the carotid wall motion from the ultrasound radio frequency signals. To extract the high-frequency components of the wall motion, wall vibration, the empirical mode decomposition was then used. The posterior wall (intima-media) motion and vibration were extracted for 54 healthy volunteers (mean age: 33.87 ± 14.73 years), including 13 overweight subjects (body mass index > 25) and 14 female participants using their radio frequency signals. The results showed that the dominant frequency of the wall vibration correlates with age ( r = −0.5887, p < 0.001) and body mass index ( r = −0.4838, p < 0.001). The quantitative analysis further demonstrated that the dominant frequency of the vibration in the radial direction of the carotid wall decreases by age and is lower in overweight subjects. Besides, the peak-to-peak amplitude of the wall vibration showed significant correlations with age ( r = −0.5456, p < 0.001) and body mass index ( r = −0.5821, p < 0.001). The peak-to-peak amplitude also decreases by age and is lower in overweight subjects. However, there were no significant correlations between these features of the wall vibrations and systolic/diastolic blood pressure and sex. Our proposed measures were certified using the calculated arterial stiffness indices. The average power spectrum of the elderly subjects’ wall motion in the frequency range of the wall vibration (>100 Hz) is decreased more in comparison with the young subjects. Our results revealed that the proposed method may be useful for detecting the stiffness and distortion in the carotid wall that occur prior to wall thickening caused by age as an early-stage atherosclerotic sign.

Detection and identification of first and second heart sounds using empirical mode decomposition

We present a novel, low complexity method for the detection of the first and second of heart sounds (S1 and S2, respectively) and the periods of systole and diastole without using an electrocardiogram reference. The algorithm uses a technique called empirical mode decomposition to produce intensity envelopes of the main heart sounds in the time domain. The performance of the algorithm was evaluated using 14,000 cardiac periods from 100 normal and abnormal digital phonocardiographic recordings. The sensitivity of the detection method was 88.3% for both S1 and S2, and the precision (positive predictive value) was 95.8% for both S1 and S2.

The analysis of the artifacts due to the simultaneous use of two ultrasound probes with different/similar operating frequencies

The ultrasound imaging has the potential to become a dominant technique for noninvasive therapies and least invasive surgeries. Few cases may require using multiple probes of different units with different modes of ultrasound on the same patient. It generates imaging artifacts, which makes it complicated to gather information from the acquired image. This study was to identify and analyse the artifacts which are produced by simultaneous use of two probes with different/same operating frequencies. Six imaging studies were performed. First of all, the imaging artifacts of the 3.5 MHz and 6 MHz center frequencies with similar (longitudinal) positions of the probes. Secondly, with similar operating frequencies the 6 MHz probe changed from longitudinal to transverse placement to analyse the resulting artifacts. The third study was done with transverse placement of 3.5 MHz probe. The rest of the three cases were just the repetition with common pulse frequencies. Such artifacts in 3D ultrasound images are more obscure than the other artifacts associated and reported.

Repeatability of popliteal blood flow and lower limb vascular conductance at rest and exercise during body tilt using Doppler ultrasound

We tested the data repeatability for popliteal blood flow velocity (PBV), popliteal arterial diameter (AD(pop)), popliteal blood flow (PBF) and lower limb vascular conductance (VC) at rest and exercise in three body positions, two work rates and two inspired oxygen fractions. Fifteen, eleven and ten healthy volunteers participated in the three phases of the studies. Resting protocols were performed in horizontal (HOR), 35° head-down tilt (HDT) and 45° head-up tilt (HUT) for 5 min in each body position. Participants also exercised at lower and higher power outputs (repeated plantar flexion contractions at 20% and 30% maximal voluntary contraction, respectively) in HOR, HDT and HUT and in normoxia (21%O2) and hypoxia (14%O2) with the same work rates and body positions. PBV and AD(pop) were measured by ultrasound to determine PBF, and VC was estimated by dividing PBF by muscle perfusion pressure (MPP). PBV, AD(pop), PBF and VC were not different, demonstrated good agreement and consistency between the two days of testing during both rest and exercise conditions regardless of body position. Therefore, these data support the utilization of Doppler and echo Doppler ultrasound as a reproducible method to measure PBV and AD(pop) and consequently estimate PBF and VC responses in such conditions.

Tissue artifact removal from respiratory signals based on empirical mode decomposition

On-line measurement of respiration plays an important role in monitoring human physical activities. Such measurement commonly employs sensing belts secured around the rib cage and abdomen of the test object. Affected by the movement of body tissues, respiratory signals typically have a low signal-to-noise ratio. Removing tissue artifacts therefore is critical to ensuring effective respiration analysis. This paper presents a signal decomposition technique for tissue artifact removal from respiratory signals, based on the empirical mode decomposition (EMD). An algorithm based on the mutual information and power criteria was devised to automatically select appropriate intrinsic mode functions for tissue artifact removal and respiratory signal reconstruction. Performance of the EMD-algorithm was evaluated through simulations and real-life experiments (N = 105). Comparison with low-pass filtering that has been conventionally applied confirmed the effectiveness of the technique in tissue artifacts removal.

A simulator for mixed Doppler ultrasound signals from pulsatile blood flow and vessel wall with mild stenosis

A novel computer simulator to generate mixed Doppler ultrasound signals from the pulsatile blood flow and vessel wall with mild stenosis is presented in this paper. In-phase and quadrature Doppler blood flow signals are generated using cosine-superposed components modulated by a spectrogram estimated from velocity profile. Meanwhile,Doppler signals echoed from bidirectional moving walls are generated with the input waveforms of the wall velocities. Finally, the Doppler signals determined above are summed respectively to yield the combined Doppler signals in terms of given sample volume shape. The experimental results show that the proposed simulator generates mixed Doppler signals with the characteristics similar to those found in practice, and could be an useful experimental data source for evaluating the performance of wall filters.

A computer simulation model for Doppler ultrasound signals from pulsatile blood flow in stenosed vessels

A computer simulation model based on an analytic flow velocity distribution is proposed to generate Doppler ultrasound signals from pulsatile blood flow in the vessels with various stenosis degrees. The model takes into account the velocity field from pulsatile blood flow in the stenosed vessels, sample volume shape and acoustic factors that affect the Doppler signals. By analytically solving the Navier-Stokes equations, the velocity distributions of pulsatile blood flow in the vessels with various stenosis degrees are firstly calculated according to the velocity at the axis of the circular tube. Secondly, power spectral density (PSD) of the Doppler signals is estimated by summing the contribution of all scatterers passing through the sample volume grouped into elemental volumes. Finally, Doppler signals are generated using cosine-superposed components that are modulated by the PSD functions that vary over the cardiac cycle. The results show that the model generates Doppler blood flow signals with characteristics similar to those found in practice. It could be concluded that the proposed approach offers the advantages of computational simplicity and practicality for simulating Doppler ultrasound signals from pulsatile blood flow in stenosed vessels.

Automatic contrast enhancement using ensemble empirical mode decomposition

Ultrasound nonlinear contrast imaging using microbubble-based contrast agents has been widely investigated. However, the degree of contrast enhancement is often limited by overlap between the spectra of the tissue and microbubble nonlinear responses, which makes it difficult to separate them. The use of ensemble empirical mode decomposition (EEMD) in the Hilbert-Huang transform (HHT) was previously explored with the aim of alleviating this problem. The HHT is designed for analyzing nonlinear and nonstationary data, whereas EEMD is a method associated with the HHT that allows decomposition of data into a finite number of intrinsic mode functions (IMFs). It was found that the contrast can be effectively improved in certain IMFs, but manual selection of appropriate IMFs is still required. This prompted the present study to test the hypothesis that the contrast can be enhanced without requiring manual selection by summing appropriately weighted IMFs and demodulating the signal at appropriate frequencies. That is, a data-driven mechanism for determining weights and demodulation frequencies was derived and tested. Phantom results show that an overall contrast enhancement of up to 12.5 dB can be achieved. A fused-image representation that simultaneously displays the conventional B-mode image and the new contrast-mode image is also presented.

Automatic motion and noise artifact detection in Holter ECG data using empirical mode decomposition and statistical approaches

We present a real-time method for the detection of motion and noise (MN) artifacts, which frequently interferes with accurate rhythm assessment when ECG signals are collected from Holter monitors. Our MN artifact detection approach involves two stages. The first stage involves the use of the first-order intrinsic mode function (F-IMF) from the empirical mode decomposition to isolate the artifacts' dynamics as they are largely concentrated in the higher frequencies. The second stage of our approach uses three statistical measures on the F-IMF time series to look for characteristics of randomness and variability, which are hallmark signatures of MN artifacts: the Shannon entropy, mean, and variance. We then use the receiver-operator characteristics curve on Holter data from 15 healthy subjects to derive threshold values associated with these statistical measures to separate between the clean and MN artifacts' data segments. With threshold values derived from 15 training data sets, we tested our algorithms on 30 additional healthy subjects. Our results show that our algorithms are able to detect the presence of MN artifacts with sensitivity and specificity of 96.63% and 94.73%, respectively. In addition, when we applied our previously developed algorithm for atrial fibrillation (AF) detection on those segments that have been labeled to be free from MN artifacts, the specificity increased from 73.66% to 85.04% without loss of sensitivity (74.48%-74.62%) on six subjects diagnosed with AF. Finally, the computation time was less than 0.2 s using a MATLAB code, indicating that real-time application of the algorithms is possible for Holter monitoring.

Hilbert-Huang transform for analysis of heart rate variability in cardiac health

This paper introduces a modified technique based on Hilbert-Huang transform (HHT) to improve the spectrum estimates of heart rate variability (HRV). In order to make the beat-to-beat (RR) interval be a function of time and produce an evenly sampled time series, we first adopt a preprocessing method to interpolate and resample the original RR interval. Then, the HHT, which is based on the empirical mode decomposition (EMD) approach to decompose the HRV signal into several monocomponent signals that become analytic signals by means of Hilbert transform, is proposed to extract the features of preprocessed time series and to characterize the dynamic behaviors of parasympathetic and sympathetic nervous system of heart. At last, the frequency behaviors of the Hilbert spectrum and Hilbert marginal spectrum (HMS) are studied to estimate the spectral traits of HRV signals. In this paper, two kinds of experiment data are used to compare our method with the conventional power spectral density (PSD) estimation. The analysis results of the simulated HRV series show that interpolation and resampling are basic requirements for HRV data processing, and HMS is superior to PSD estimation. On the other hand, in order to further prove the superiority of our approach, real HRV signals are collected from seven young health subjects under the condition that autonomic nervous system (ANS) is blocked by certain acute selective blocking drugs: atropine and metoprolol. The high-frequency power/total power ratio and low-frequency power/high-frequency power ratio indicate that compared with the Fourier spectrum based on principal dynamic mode, our method is more sensitive and effective to identify the low-frequency and high-frequency bands of HRV.

Noise-assisted data processing with empirical mode decomposition in biomedical signals

In this paper, a methodology is described in order to investigate the performance of empirical mode decomposition (EMD) in biomedical signals, and especially in the case of electrocardiogram (ECG). Synthetic ECG signals corrupted with white Gaussian noise are employed and time series of various lengths are processed with EMD in order to extract the intrinsic mode functions (IMFs). A statistical significance test is implemented for the identification of IMFs with high-level noise components and their exclusion from denoising procedures. Simulation campaign results reveal that a decrease of processing time is accomplished with the introduction of preprocessing stage, prior to the application of EMD in biomedical time series. Furthermore, the variation in the number of IMFs according to the type of the preprocessing stage is studied as a function of SNR and time-series length. The application of the methodology in MIT-BIH ECG records is also presented in order to verify the findings in real ECG signals.

Assessment of arterial distension based on continuous wave Doppler ultrasound with an improved Hilbert-Huang processing

A novel approach based on continuous wave (CW) Doppler ultrasound and the Hilbert-Huang transform with end-effect restraint by mirror extending is proposed to assess arterial distension. In the approach, bidirectional Doppler signals were first separated using the phasing-filter technique from the mixed quadrature Doppler signals, which were produced by bidirectional blood and vessel wall movements. Each separated unidirectional signal was decomposed into intrinsic mode functions (IMFs) using the empirical mode decomposition with end effect restraint by mirror extending algorithm, and then the relevant IMFs that contribute to the vessel wall components were identified. Finally, the displacement waveforms of the vessel wall were calculated by integrating its moving velocity waveforms, which were extracted from the bidirectional Hilbert spectrum estimated from the identified wall IMFs. This approach was applied to simulated and clinical Doppler signals from normal common carotid arteries (CCAs). In the simulation study, the estimated wall moving velocity and displacement waveforms were compared with the theoretical ones, respectively. The mean and standard deviation of the root-mean-square errors between the estimated and theoretical wall distension of the 30 realizations was 4.2 +/- 0.4 microm. In the clinical study, peak-to-peak distension was extracted in a subject and then averaged over 15 cardiac cycles, resulting in 603 +/- 22 microm. The mean and standard deviation of the CCA distension averaged over the experimental measurements of 12 healthy subjects gave the result of 620 +/- 154 microm. The clinical results were in agreement with those measured by using the multigate Doppler ultrasound and echo tracking systems. The results show that based on the CW Doppler ultrasound, the proposed approach is practical for extracting arterial wall peak-to-peak distension correctly and could be an alternative method for the vessel wall distension estimation.

Potential contrast improvement in ultrasound pulse inversion imaging using EMD and EEMD

Ultrasound nonlinear imaging using microbubble-based contrast agents has been widely investigated. Nonetheless, its contrast is often reduced by the nonlinearity of acoustic wave propagation in tissue. In this paper, we explore the use of empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) in the Hilbert-Huang transform (HHT) for possible contrast improvement. The HHT is designed for analyzing nonlinear and nonstationary data, whereas EMD is a method associated with the HHT that allows decomposition of data into a finite number of intrinsic modes. The hypothesis is that the nonlinear signal from microbubbles and the tissue nonlinear signal can be better differentiated with EMD and EEMD, thus making contrast improvement possible. Specifically, we tested this method on pulse-inversion nonlinear imaging, which is generally regarded as one of the most effective nonlinear imaging methods. The results show that the contrast-to-tissue ratios at the fundamental and second-harmonic frequencies were improved by 10.2 and 4.3 dB, respectively, after EEMD. Nonetheless, image artifacts also appeared, and hence further investigation is needed before EMD and EEMD can be applied in practical applications of ultrasound nonlinear imaging.

Preservation of quadrature Doppler signals from bidirectional slow blood flow close to the vessel wall using an adaptive decomposition algorithm

A novel approach based on the phasing-filter (PF) technique and the empirical mode decomposition (EMD) algorithm is proposed to preserve quadrature Doppler signal components from bidirectional slow blood flow close to the vessel wall. Bidirectional mixed Doppler ultrasound signals, which were echoed from the forward and reverse moving blood and vessel wall, were initially separated to avoid the phase distortion of quadrature Doppler signals (which is induced from direct decomposition by the nonlinear EMD processing). Separated unidirectional mixed Doppler signals were decomposed into intrinsic mode functions (IMFs) using the EMD algorithm and the relevant IMFs that contribute to blood flow components were identified and summed to give the blood flow signals, whereby only the components from the bidirectional slow blood flow close to the vessel wall were retained independently. The complex quadrature Doppler blood flow signal was reconstructed from a combination of the extracted unidirectional Doppler blood flow signals. The proposed approach was applied to simulated and clinical Doppler signals. It is concluded from the experimental results that this approach is practical for the preservation of quadrature Doppler signal components from the bidirectional slow blood flow close to the vessel wall, and may provide more diagnostic information for the diagnosis and treatment of vascular diseases.