Radio frequency ultrasound time series signal analysis to evaluate high-intensity focused ultrasound lesion formation status in tissue

Abstract

High-intensity focused ultrasound (HIFU) is a novel treatment modality used by scientists and clinicians in the recent decades. This modality has had a great and significant success as a noninvasive surgery technique applicable in tissue ablation therapy and cancer treatment. In this study, radio frequency (RF) ultrasound signals were acquired and registered in three stages of before, during, and after HIFU exposures. Different features of RF time series signals including the sum of amplitude spectrum in the four quarters of the frequency range, the slope, and intercept of the best-fit line to the entire power spectrum and the Shannon entropy were utilized to distinguish between the HIFU-induced thermal lesion and the normal tissue. We also examined the RF data, frame by frame to identify exposure effects on the formation and characteristics of a HIFU thermal lesion at different time steps throughout the treatment. The results obtained showed that the spectrum frequency quarters and the slope and intercept of the best fit line to the entire power spectrum both increased two times during the HIFU exposures. The Shannon entropy, however, decreased after the exposures. In conclusion, different characteristics of RF time series signal possess promising features that can be used to characterize ablated and nonablated tissues and to distinguish them from each other in a quasi-quantitative fashion.

Identification of Denatured Biological Tissues Based on Improved Variational Mode Decomposition and Autoregressive Model during HIFU Treatment

“…With the development of nonlinear technology, many nonlinear computational methods are proposed to analyze various systems [23][24][25]. Researchers in the field of ultrasound have studied the characteristics of ultrasonic energy, sound speed, information entropy, and sample entropy of ultrasonic echo signals, hoping to find characteristics that can accurately reflect the characteristics of biological tissues [26][27][28][29][30]. In [26], the energy characteristics of ultrasonic signals were used to recognize the denatured biological tissue during HIFU treatment.…”

“…In [28,29], the biological tissue state after HIFU irradiation was assessed by the information entropy of the radio frequency ultrasound. In [30], the sample entropy of the radio frequency ultrasonic signals was employed to distinguish the normal porcine tissues and denatured porcine tissues. However, all the methods mentioned above have some disadvantages, such as sound speed measurement of biological tissues is easily affected by environmental noise, which leads to the inaccuracy of the recognition results of denatured biological tissue.…”

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

“…The results showed that there was a big difference between the Shannon entropy value of the ultrasonic signal of the normal biological tissues and the damaged biological tissues. In [19], the multi-scale sample entropy (MSE) of the radio frequency ultrasonic signals was employed to monitor the damage of porcine tissues. Compared with the Shannon entropy and MSE mentioned above, multi-scale permutation entropy (MPE) has the advantages including strong noise resistance and robustness, so the MPE is often employed to analyze the complexity of ultrasonic time series [20][21][22].…”

Identification of Denatured Biological Tissues Based on Compressed Sensing and Improved Multiscale Dispersion Entropy during HIFU Treatment

“…In [ 23 , 24 ], values of acoustic absorption and sound velocity were used to predict temperature rise and estimate treatment. In [ 25 , 26 ], biological tissue state after HIFU irradiation was assessed by information entropy of the radio frequency ultrasound. Comparing with ultrasonic attenuation, sound velocity, and information entropy, multiscale permutation entropy (MPE) can analyze sequence information more efficiently.…”