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In this paper, harmonic concise atoms for the mean scatterer spacing (MSS) estimation was proposed to distinguish the normal and the ablated liver tissues. First, the second harmonic RF echo signals are separated with the Butterworth bandpass filter from the ultrasound RF echo signals, and a series of Gabor atoms are found to represent the local components with the concentrated energy. Then, the concise atoms are selected from the found Gabor atoms to optimal match the coherent components in the harmonic signals. Finally, the locations of the adjacent concise atoms are used to estimate the MSSs. In the microwave ablation experiments, the fundamental and second-harmonic RF echo signals were collected from the regions of interest in the normal untreated porcine tissues and thermally coagulated tissues ablated for 300 s, respectively. The MSSs, as well as the means and standard deviations of the MSSs were calculated from the collected signals for further comparation. The experiment results showed that the harmonic-based MSSs had large difference between the normal and the ablated liver tissues. The decrement in the harmonic-based mean MSS, and standard deviation in the ten tissues were 56.33 %, and 8.33 % greater than the fundamental-based results. It indicated that the harmonic-based MSS results with the concise atoms could improve the MSS estimation accuracy to distinguish the normal and the thermal coagulated liver tissues induced by the microwave ablation.

Pulse wave velocity (PWV) is currently the gold standard for quantitatively measuring arterial stiffness, which is of great significance for the early diagnosis and effective prevention of cardio-cerebral vascular diseases. With the development of the ultrafast ultrasound imaging based on the plane wave technique, the ultrasonic transit time-based method with straightforward principle is preferable for the detection of the local PWV of carotid artery. However, the filtering parameter settings in this method are difficult to be flexibly controlled, and the results of pulse wave transit time estimations are not stable. Aiming at these problems, an adaptive local PWV estimation based on the particle swarm optimization algorithm is proposed. First, An improved particle swarm optimization (PSO) with a fitness function evaluated in terms of the Savitzky-Golay (SG) filter length, SG polynomial degree, tracking position and tracking points parameters in regional upstroke tracking (RUT) approach is used to find the most optimum parameter settings for PWV estimation. To quantitatively evaluate the proposed adaptive local PWV estimation method (PSO_RUT), an ultrasound simulation model for three age groups is used. Results show that the mean PWV deviations from theoretical values estimated by the proposed PSO_RUT method are just within 3.8%, and lower than that estimated by RUT method. Therefore, the PSO_RUT approach proposed in this study can provide effective means for the improvement on the accuracy of ultrasound-based carotid PWV estimation.

In this paper, red blood cell aggregation classification based on hyper spectral analysis of ultrasonic radiofrequency (RF) echo signals is proposed. Firstly, Morlet wavelet is applied to the sub-band decomposition of ultrasonic RF echo signals. Then, five statistical features including mean, variance, median, kurtosis and root mean square of each sub-band are calculated to form the feature vectors. 18 kinds of blood with different red blood cell concentration-aggregation are taken as samples, then multi-frame ultrasonic RF echo signals are collected using ultrasonic linear array probe. The region of interest (ROI) is selected from the B-mode image of a certain frame. 20 subbands are obtained by the hyper spectral analysis of each line of ultrasonic RF echo signals in the ROI. Five statistical features of each sub-band are calculated, and then the feature vectors are obtained after local normalization. Finally, support vector machine (SVM) and random forest classifiers are used to classify the feature vectors respectively. The overall average classification accuracy of SVM is $91.43\pm 6.17\%$ , and the overall average classification accuracy of random forest classifier is 96.19 ± 4.28 %.