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

Abstract

Identification of denatured biological tissue is crucial to high-intensity focused ultrasound (HIFU) treatment, which can monitor HIFU treatment and improve treatment efficiency. In this paper, a novel method based on compressed sensing (CS) and improved multiscale dispersion entropy (IMDE) is proposed to evaluate the complexity of ultrasonic scattered echo signals during HIFU treatment. In the analysis of CS, the method of orthogonal matching pursuit (OMP) is employed to reconstruct the denoised signal. CS-OMP can denoise the ultrasonic scattered echo signal effectively. Comparing with traditional multiscale dispersion entropy (MDE), IMDE improves the coarse-grained process in the multiscale analysis, which improves the stability of MDE. In the analysis of simulated signals, the entropy value of the IMDE method has less fluctuation compared with MDE, indicating that the IMDE method has better stability. In addition, MDE and IMDE are applied to the 300 cases of ultrasonic scattered echo signals after denoising (including 150 cases of normal tissues and 150 cases of denatured tissues). The experimental results show that the MDE and IMDE values of denatured tissues are higher than normal tissues. Both the MDE and IMDE method can be used to identify whether biological tissue is denatured. However, the multiscale entropy curve of IMDE is smoother and more stable than MDE. The interclass distance of IMDE is greater than MDE, and the intraclass distance of IMDE is less than MDE at different scale factors. This indicates that IMDE can better distinguish normal tissues and denatured tissues to obtain more accurate clinical diagnosis during HIFU treatment.

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

“…According to the reference [29], Gaussian white noise with 5000 sampling points is used as a simulation signal to prove the advantages of IMWPE compare with the MPE method. Both MPE and IMWPE methods are analyzed for the simulation signal.…”

Recognition of Biological Tissue Denaturation Based on Improved Multiscale Permutation Entropy and GK Fuzzy Clustering

“…Many nonlinear feature algorithms, including Shannon entropy, sample entropy, wavelet entropy, and permutation entropy, are widely applied to extract the features of ultrasonic signal and identify the different states of biological tissue denaturation [21][22][23][24][25][26][27][28][29]. Especially, permutation entropy is a nonlinear analysis algorithm for the time series complexity calculation based on phase space reconstruction.…”