Zhenyu Guo

A review of electrical impedance techniques for breast cancer detection.

Some evidence has been found that malignant breast tumors have lower electrical impedance than surrounding normal tissues. Although the separation of malignant tumors from benign lesions based on impedance measurements needs further investigation, electrical impedance could be used as an indicator for breast cancer detection. In this paper, we provide a systematic technical review of the existing electrical impedance techniques proposed for breast cancer detection, with an emphasis on noninvasive impedance imaging techniques. The electrical impedance of human breast tissue is first introduced, with tabulation of previous in vitro impedance measurement results on cancerous and normal breast tissues, and a brief description on the limited in vivo impedance measurements completed with invasive, or noninvasive, non-imaging techniques. A detailed review on noninvasive impedance imaging techniques for breast cancer detection, such as electrical impedance tomography (EIT) and electrical impedance mapping (EIM), is then presented. We suggest that for better breast cancer detection, an invasive impedance technique may be enhanced by combination with other cancer indicators. 3D EIT should be improved through collective efforts. EIM using a pair of electrode arrays is a viable method with great potential. Magnetic induction tomography and other magnetic induction based impedance imaging for breast cancer detection are promising and merit further exploration as well.

Comparison of time-frequency distribution techniques for analysis of simulated Doppler ultrasound signals of the femoral artery

The time-frequency distribution of the Doppler ultrasound blood flow signal is normally computed by using the short-time Fourier transform or autoregressive modeling. These two techniques require stationarity of the signal during a finite interval. This requirement imposes some limitations on the distribution estimate. In the present study, three new techniques for nonstationary signal analysis (the Choi-Williams distribution, a reduced interference distribution, and the Bessel distribution) were tested to determine their advantages and limitations for analysis of the Doppler blood flow signal of the femoral artery. For the purpose of comparison, a model simulating the quadrature Doppler signal was developed, and the parameters of each technique were optimized based on the theoretical distribution. Distributions computed using these new techniques were assessed and compared with those computed using the short-time Fourier transform and autoregressive modeling. Three indexes, the correlation coefficient, the integrated squared error, and the normalized root-mean-squared error of the mean frequency waveform, were used to evaluate the performance of each technique. The results showed that the Bessel distribution performed the best, but the Choi-Williams distribution and autoregressive modeling are also techniques which can generate good time-frequency distributions of Doppler signals.<<ETX>>

The time-frequency distributions of nonstationary signals based on a Bessel kernel

A kernel based on the first kind Bessel function of order one is proposed to compute the time-frequency distributions of nonstationary signals. This kernel can suppress the cross terms of the distribution effectively. It is shown that the Bessel distribution (the time-frequency distribution using Bessel kernel) meets most of the desirable properties with high time-frequency resolution. A numerical alias-free implementation of the distribution is presented. Examples of applications in time-frequency analysis of the hearts sound and Doppler blood flow signals are given to show that the Bessel distribution can be easily adapted to two very different signals for cardiovascular signal processing. By controlling a kernel parameter, this distribution can be used to compute the time-frequency representations of transient deterministic and random signals. The study confirms the potentials of the proposed distribution in nonstationary signal analysis. >

Three-dimensional power Doppler imaging: A phantom study to quantify vessel stenosis

This study investigated whether three-dimensional (3D) power Doppler imaging can be used to quantify arterial stenosis and its potential as an alternative to x-ray angiography. Three-dimensional power Doppler images of in vitro stenotic vessels were generated under different hemodynamic conditions with a 3D power Doppler imaging system. This system includes: a Macintosh Quadra 840AV computer used to perform 3D imaging acquisition, reconstruction and display; a computer-controlled motor-driven translation assembly used to move the transducer; and an ATL Ultramark 9 HDI ultrasound system. Three vascular- and tissue-mimicking phantoms containing three wall-less stenotic vessels with area reduction of 80%, 50% and 30% were imaged with different flow rates under both steady and pulsatile flow conditions and with different Doppler angles under steady flow condition. With the use of the blood mimic, experimental results demonstrated that power Doppler imaging is nearly independent on flow velocity and Doppler angle. It was also demonstrated that 3D power Doppler imaging can produce nonpulsatile angiographic-like 3D images of the flow field. The stenotic vessels were quantified with an overall accuracy of 8.3% of the vessel area and an overall precision of 7% of the vessel area under the conditions described in this paper. It is believed that 3D power Doppler imaging can be used to quantify arterial stenosis, and in some applications it could be an alternative to x-ray angiography.

A comparison of the wavelet and short-time fourier transforms for Doppler spectral analysis

Doppler spectrum analysis provides a non-invasive means to measure blood flow velocity and to diagnose arterial occlusive disease. The time-frequency representation of the Doppler blood flow signal is normally computed by using the short-time Fourier transform (STFT). This transform requires stationarity of the signal during a finite time interval, and thus imposes some constraints on the representation estimate. In addition, the STFT has a fixed time-frequency window, making it inaccurate to analyze signals having relatively wide bandwidths that change rapidly with time. In the present study, wavelet transform (WT), having a flexible time-frequency window, was used to investigate its advantages and limitations for the analysis of the Doppler blood flow signal. Representations computed using the WT with a modified Morlet wavelet were investigated and compared with the theoretical representation and those computed using the STFT with a Gaussian window. The time and frequency resolutions of these two approaches were compared. Three indices, the normalized root-mean-squared errors of the minimum, the maximum and the mean frequency waveforms, were used to evaluate the performance of the WT. Results showed that the WT can not only be used as an alternative signal processing tool to the STFT for Doppler blood flow signals, but can also generate a time-frequency representation with better resolution than the STFT. In addition, the WT method can provide both satisfactory mean frequencies and maximum frequencies. This technique is expected to be useful for the analysis of Doppler blood flow signals to quantify arterial stenoses.

Quantitative investigation of in vitro flow using three-dimensional colour Doppler ultrasound

A quantitative in vitro flow study was performed by using a three-dimensional colour Doppler imaging system. This system was based on a clinical ultrasound instrument with its transducer mounted on a motor-driven translation stage. A vascular and tissue-mimicking phantom containing two wall-less vessels, one normal and another stenotic, was used to quantify the measurement accuracy of the flow velocity and the flow field. Steady state flows, having Reynolds numbers ranging between 460 and 1300, were generated by a computer-controlled positive displacement pump. Effects of the parameter settings of the ultrasound instrument on results of the estimation of flow field were also studied. Experimental results show that our three-dimensional colour Doppler systems velocity accuracy was better than 7% of the Nyquist velocity and its spatial accuracy was better than 0.5 mm. The system showed a good correlation (r = 0.999) between the estimated and the true mean flow velocity, and a good correlation (r = 0.998) between the estimated maximum and the true mean flow velocity. This study is our first step toward validating the measurement of the three-dimensional velocity and wall shear stress distributions by using three-dimensional colour Doppler ultrasound

Artificial neural networks in computer-assisted classification of heart sounds in patients with porcine bioprosthetic valves.

The paper describes the design, training and testing of a three-layer feedforward back-propagation neural network for the classification of bioprosthetic valve closure sounds. Forty-seven patients with a porcine bioprosthetic valve inserted in the aortic position were involved in the study. Twenty-four of them had a normal bioprosthetic valve, and the other 23 had a degenerated valve. Five features extracted from the Fourier spectra and 12 linear predictive coding (LPC) coefficients of the sounds were used separately as the input of two neural-network classifiers. The performance of the classifiers was tested using the leave-one-out method. Results show that correct classifications were 85 per cent using the spectral features, and 89 per cent using the LPC coefficients. The study confirms the potential of artificial networks for the classification of bioprosthetic valve closure sounds. Clinical use of this method, however, still requires further investigation.

Time-frequency analysis of the first heart sound. Part 2: An appropriate time-frequency representation technique

A simulated first heart sound (S1) signal is used to determine the best technique for analysing physiological S1 from the following five time-frequency representations (TFR): the spectrogram, time-varying autoregressive modelling, binomial reduced interference distribution, Bessel distribution and cone-kernel distribution (CKD). To provide information on the time and frequency resolutions of each TFR technique, the instantaneous frequency and the −3 dB bandwidth as functions of time were computed for each simulated component of the S1. The performance index for selecting the best technique was based on the relative error and the correlation coefficient of the instantaneous frequency function between the theoretical distribution and the computed TFR. This index served to select the best technique. The sensitivity of each technique to noise and to small variations of the signal parameters was also evaluated. The results of the comparative study show that, although important limitations were found for all five TFRs tested, the CKD appears to be the best technique for the time-frequency analysis of multicomponent signals such as the simulated S1.

Analysis of the first heart sound using the matching pursuit method.

It is acknowledged that the first heart sound S1 consists of two major, high-frequency components M1 and T1, corresponding, respectively, to the vibrations of the mitral and tricuspid valves and their surrounding tissues following valve closure in early systeole. In this study, the matching pursuit (MP) method was used to decompose S1 into a series of time-frequency atoms. M1 and T1 were separated from the parameterised atoms of S1. The first two dominant frequencies of M1 were identified and used as features of a linear classifer to diagnose mitral valve abnormality. This method was applied to two sets of S1 data recorded from 15 patients with normal, and 15 patients with abnormal, bioprosthetic mitral valves, respectively. It was found that the two features exhibit significant differences between the normal and abnormal sets (p<0.001). Using these two features, a correct classification of 93% was obtained. In addition, when the Wigner distribution of S1 was calculated from the decomposed atoms and compared with a spectrogram, the MP method provided better results. The study demonstrates that the MP method may be a promising technique for heart sound analysis.

Cardiac doppler blood-flow signal analysis

The normality (Gaussian property) and stationarity of the cardiac Doppler blood-flow signal were evaluated on short-time segments distributed over the cardiac cycle. The basic approaches used to perform statistical tests on the nonstationary and quasiperiodic cardiac Doppler signal are presented. The results obtained from the data of ten patients having a normal aortic valve and ten patients having a stenotic valve indicate that a complex Gaussian random process is an acceptable approximation for the clinical cardiac Doppler signal. For segments of 10 ms or less, 82 per cent of them were accepted to be stationary with a significance level of 0.05, whereas for durations greater than 40 ms, the percentage of stationary segments was less than 75 per cent. It was concluded that the 10ms window generally used in practice is a good choice for Doppler spectrogram estimation, but a shorter time interval would be preferable.