Investigation on effect of transmit condition on ultrasonic measurement of 2D motion velocity

A phase-sensitive 2D motion estimator is useful for measurement of minute tissue motion. However, the effect of conditions for emission of ultrasonic waves on the accuracy of such an estimator has not been investigated thoroughly. In the present study, the accuracy of the phase-sensitive 2D motion estimator was evaluated under a variety of transmission conditions. Although plane wave imaging with a single emission per frame achieved an extremely high temporal resolution of 10417 Hz, the accuracy in estimation of lateral velocities was worse than compound-based method or focused-beam method. By contrast, the accuracy in estimation of axial velocities hardly depended on the transmission conditions. Also, the phase-sensitive 2D motion estimator was combined with the block matching method to estimate displacements larger than the ultrasonic wavelength. Furthermore, the results show that the correlation coefficient in block matching has potential to be used for evaluation of the reliability of the estimated velocity.

Dispersive MUSIC algorithm for Lamb wave phased array

By controlling the excitation time delay on each element, the conventional phased array can physically focus signals transmitted by different elements on a desired point in turn. An alternative and time-saving strategy is that every element takes turns to transmit the excitation and the remaining elements receive the corresponding response signals, which is known as the full matrix capture (FMC) method for data acquisition, and then let the signals virtually focus on every desired point by post-processing technique. In this study, based on the FMC, a dispersive multiple signal classification (MUSIC) algorithm for Lamb wave phased array is developed to locate defects. The virtual time reversal is implemented to back propagate the wave packets corresponding to the desired focusing point and a window function is adopted to adaptively isolate the desired packets from the other components. Then those wave packets are forward propagated to the original focusing point at a constant velocity. For every potential focusing point and all receivers, the virtual array focuses the signals from all transmitters so as to obtain the focusing signals. The MUSIC algorithm with the obtained focusing signals is adopted to achieve Lamb wave imaging. Benefiting from the post-processing operations, the baseline subtraction as well as the estimation for the number of the scattering sources is no longer required in the proposed algorithm. Experiments on an aluminum plate with three artificial defects and a compact circular PZT array are implemented and the results demonstrate the efficacy of the proposed algorithm.

Enhancement of reflection and backscattering components by plane wave imaging for estimation of surface roughness

In the early stage of atherosclerosis, the luminal surface of the arterial wall becomes rough. Methods for distinguishing between the reflected and backscattered components in the ultrasonic echo from the arterial wall has the potential to be used as a method for assessment of the roughness of the arterial wall. In this study, we proposed a method to distinguish between the reflected and backscattered components using a technique based on plane wave compounding. This method was evaluated by experiments using planar phantoms with rough surfaces made of polyurethane rubber. The coefficient of variation calculated from the mean value of the reflection component and the standard deviation of the backscattering component was proportional to the roughness of the rubber phantom. This result shows the potential usefulness of this method for analyzing surface roughness of the arterial wall.

Application of an Automatic Noise or Signal Removal Algorithm Based on Synchrosqueezed Continuous Wavelet Transform of Passive Surface Wave Imaging: A Case Study in Sichuan, China

Passive surface wave imaging based on noise cross-correlation has been a research hotspot in recent years. However, because randomness of noise is difficult to achieve in reality, prominent noise sources will inevitably affect the dispersion measurement. Additionally, in order to recover high-fidelity surface waves, the time series input during cross-correlation calculation is usually very long, which greatly limits the efficiency of passive surface wave imaging. With an automatic noise or signal removal algorithm based on synchrosqueezed continuous wavelet transform (SS-CWT), these problems can be alleviated. We applied this method to 1-h passive datasets acquired in Sichuan province, China; separated the prominent noise events in the raw field data, and enhanced the cross-correlation reconstructed surface waves, effectively improving the accuracy of the dispersion measurement. Then, using the conventional surface wave inversion method, the shear wave velocity profile of the underground structure in this area was obtained.