Identification of Denatured Biological Tissues Based on Time-Frequency Entropy and Refined Composite Multi-Scale Weighted Permutation Entropy during HIFU Treatment

Abstract

Identification of denatured biological tissue is crucial to high intensity focused ultrasound (HIFU) treatment. It is not easy for intercepting ultrasonic scattered echo signals from HIFU treatment region. Therefore, this paper employed time-frequency entropy based on generalized S-transform (GST) to intercept ultrasonic echo signals. First, the time-frequency spectra of ultrasonic echo signal is obtained by GST, which is concentrated around the real instantaneous frequency of the signal. Then the time-frequency entropy is calculated based on time-frequency spectra. The experimental results indicate that the time-frequency entropy of ultrasonic echo signal will be abnormally high when ultrasonic signal travels across the boundary between normal region and treatment region in tissues. Ultrasonic scattered echo signals from treatment region can be intercepted by time-frequency entropy. In addition, the refined composite multi-scale weighted permutation entropy (RCMWPE) is proposed to evaluate the complexity of nonlinear time series. Comparing with multi-scale permutation entropy (MPE) and multi-scale weighted permutation entropy (MWPE), RCMWPE not only measures complexity of signal including amplitude information, but also improves the stability and reliability of multi-scale entropy. The RCMWPE and MPE are applied to 300 cases of actual ultrasonic scattered echo signals (including 150 cases in normal status and 150 cases in denatured status). It is found that the RCMWPE and MPE values of denatured tissues are higher than those of the normal tissues. Both RCMWPE and MPE can be used to distinguish normal tissues and denatured tissues. However, there are fewer feature points in the overlap region between RCMWPE of denatured tissues and normal tissues compared with MPE. The intra-class distance and the inter-class distance of RCMWPE are less and greater respectively than MPE. The difference between denatured tissues and normal tissues is more obvious when RCMWPE is used as the characteristic parameter. The results of this study will be helpful to guide doctors to obtain more accurate assessment of treatment effect during HIFU treatment.

Identification of denatured and normal biological tissues based on compressed sensing and refined composite multi-scale fuzzy entropy during high intensity focused ultrasound treatment*

“…[32] The intra-class distance and the inter-class distance are widely used as indicators for feature selection of image recognition, biological information recognition, and gear wear signal recognition. [33,34] The compactness and separability of tissues status are better when the intra-class distance is smaller, and the inter-class distance is larger.…”

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

“…The subtraction images of the irradiation area are shown in Figure 5. According to the reference [23,26], the actual damaged status of biological tissues were determined by the temperature and subtraction image of the irradiation area. The porcine muscle tissue before HIFU irradiation is normal tissue, and the tissue subtraction image shows that there is no white bright block in the irradiated area according to Figure 5a.…”

“…However, MPE also has some shortcomings, such as, it loses information about the signal amplitude due to that MPE does not analyze the amplitude difference between the same ordinal modes in analyzing signal complexity. At the same time, the traditional coarsegrained method in the MPE easily causes the fluctuation of MPE value, which reduces the stability of MPE [24][25][26]. In order to solve the above problems, the traditional MPE method was improved and a new nonlinear analysis method was proposed, called improved coarse-grained multi-scale weighted permutation entropy (IMWPE).…”

The Auto-Regressive Model and Spectrum Information Entropy Judgment Method for High Intensity Focused Ultrasound Echo Signal

“…Then, the appropriate threshold under each segment number was determined to judge the types of these sampled signals. We analyzed the consistency between the results of this method and empirical judgments, and the Accuracy, Sensitivity and Specificity under each segment number were calculated using Equations ( 11)- (13). The fitting curve of the comparison results is shown in Figure 5.…”

“…Some scholars studied the effects of temperature and thermal dose on the attenuation of ultrasound in muscle, liver and kidney [12]. Additionally, some scholars used characteristic parameters of ultrasonic echo signals to identify denatured tissues [13][14][15]. Other scholars also have used the harmonic frequency of ultrasonic echo signals to explore tissue characteristics [16].…”