Influence of temperature-dependent acoustic and thermal parameters and nonlinear harmonics on the prediction of thermal lesion under HIFU ablation

Nonlinear effects of dual-frequency focused ultrasound on the on-demand regulation of acoustic droplet vaporization

Dual-frequency ultrasound has been widely employed to enhance and regulate acoustic droplet vaporization (ADV) but the role of ultrasonic nonlinear effects on it remains unclear. The main objective of this study is to investigate the influence of nonlinear effects on the control of ADV nucleation under different dual-frequency focused ultrasound conditions. ADV nucleation of PFC nanodroplets activated by nonlinear dual-frequency ultrasound was modeled and parametric studies were conducted to investigate the influence of dual-frequency ultrasound frequency and acoustic power on the degree of nonlinearity (DoN), nucleation rates and dimensions of the nucleation region in a wide parameter range. The results showed that the ultrasonic nonlinearity caused a significant decrease in peak negative pressure due to waveform distortion, which leads to a lower nucleation rate in the nonlinear model compared to that in the linear model. Furthermore, the distributions of nucleation regions were also affected by the interaction between waves of different frequencies and cloud-like spatial distributions were produced, which could be modulated by the dual-frequency ultrasound parameters and have great potentials in the spatial regulation of the ADV and customized treatment protocols in clinical applications. In addition, represented by 1.5 MHz + 3 MHz, such a dual-frequency combination of fundamental and second harmonic could effectively enhance ultrasonic nonlinear effects with relatively lower peak negative pressure and higher DoN. Therefore, nonlinear effect of the dual-frequency ultrasound plays an important role in the ADV regulation, which should be considered in the numerical model and practical applications.

Recognition study of denatured biological tissues based on multi-scale rescaled range permutation entropy

The recognition of denatured biological tissue is an indispensable part in the process of high intensity focused ultrasound treatment. As a nonlinear method, multi-scale permutation entropy (MPE) is widely used in the recognition of denatured biological tissue. However, the traditional MPE method neglects the amplitude information when calculating the time series complexity. The disadvantage will affect the recognition effect of denatured tissues. In order to solve the above problems, the method of multi-scale rescaled range permutation entropy (MRRPE) is proposed in this paper. The simulation results show that the MRRPE not only includes the amplitude information of the signal when calculating the signal complexity, but also extracts the extreme volatility characteristics of the signal effectively. The proposed method is applied to the HIFU echo signals during HIFU treatment, and the support vector machine (SVM) is used for recognition. The results show that compared with MPE and the multi-scale weighted permutation entropy (MWPE), the recognition rate of denatured biological tissue based on the MRRPE is higher, up to 96.57%, which can better recognize the non-denatured biological tissues and the denatured biological tissues.

Biological Tissue Damage Monitoring Method Based on IMWPE and PNN during HIFU Treatment

Biological tissue damage monitoring is an indispensable part of high-intensity focused ultrasound (HIFU) treatment. As a nonlinear method, multi-scale permutation entropy (MPE) is widely used in the monitoring of biological tissue. However, the traditional MPE method neglects the amplitude information when calculating the time series complexity, and the stability of MPE is poor due to the defects in the coarse-grained process. In order to solve the above problems, the method of improved coarse-grained multi-scale weighted permutation entropy (IMWPE) is proposed in this paper. Compared with the MPE, the IMWPE method not only includes the amplitude of signal when calculating the signal complexity, but also improves the stability of entropy value. The IMWPE method is applied to the HIFU echo signals during HIFU treatment, and the probabilistic neural network (PNN) is used for monitoring the biological tissue damage. The results show that compared with multi-scale sample entropy (MSE)-PNN and MPE-PNN methods, the proposed IMWPE-PNN method can correctly identify all the normal tissues, and can more effectively identify damaged tissues and denatured tissues. The recognition rate for the three kinds of biological tissues is higher, up to 96.7%. This means that the IMWPE-PNN method can better monitor the status of biological tissue damage during HIFU treatment.