High-Intensity Focused Ultrasound Lesion Detection Using Adaptive Compressive Sensing Based on Empirical Mode Decomposition

Pixel-based small-window parametric ultrasound imaging for liver tumor characterization

Purpose

Characterizing liver tumors remains a challenge in clinical practice. Ultrasound parametric imaging based on statistical distribution can enhance image contrast compared with B-mode imaging, requiring scatterers following specific distributions. This study proposes a pixel-based small-window parametric ultrasound imaging method using weighted horizontally normalized Shannon entropy (WhNSE) and fuzzy entropy (FE) to improve detectability liver tumor.

Methods

Pixel-based parametric imaging requires a sliding window to traverse across the B-mode image with the step of one pixel, while calculating the entropy by the pixel values in the window. The entropy is assigned to the center pixel of the sliding window. The entropy image is obtained after getting the entropy values of all pixels. FE and WhNSE are two novel entropies first applied to parametric imaging. The detection abilities of regions of interest (ROI) and the contrast-to-noise ratio (CNR) were evaluated through simulations and clinical explorations.

Results

In simulations, FE imaging showed the highest improvement in detecting hyperechoic ROIs, with a CNR gain up to 457.31% (p < 0.01) in simulations. WhNSE imaging demonstrated the best performance in hyperechoic ROI detection, with a CNR of 1.607 ± 0.816 (p = 0.05), significantly higher than B-mode images.

Conclusions

The proposed pixel-based parametric imaging method based on fuzzy entropy and weighted horizontally normalized Shannon entropy both effectively enhance the contrast and detectability of ultrasound images. The imaging enhancement method of the pixel-based fuzzy entropy imaging with proper parameters got better detection performance, due to the consideration of the relationship of neighboring pixels.

Noninvasive Monitoring of Tissue Temperature Changes Induced by Focused Ultrasound Exposure using Sparse Expression of Ultrasonic Radio Frequency Echo Signals

Background

Noninvasive therapies such as focused ultrasound were developed to be used for cancer therapies, vessel bleeding, and drug delivery. The main purpose of focused ultrasound therapy is to affect regions of interest (ROI) of tissues without any injuries to surrounding tissues. In this regard, an appropriate monitoring method is required to control the treatment.

Methods

This study is aimed to develop a noninvasive monitoring technique of focused ultrasound (US) treatment using sparse representation of US radio frequency (RF) echo signals. To this end, reasonable results in temperature change estimation in the tissue under focused US radiation were obtained by utilizing algorithms related to sparse optimization as orthogonal matching pursuit (OMP) and accompanying Shannon's entropy. Consequently, ex vivo tissue experimental tests yielded two datasets, including low-intensity focused US (LIFU) and high-intensity focused US (HIFU) data. The proposed processing method analyzed the ultrasonic RF echo signal and expressed it as a sparse signal and calculated the entropy of each frame.

Results

The results indicated that the suggested approach could noninvasively estimate temperature changes between 37°C and 47°C during LIFU therapy. In addition, it represented temperature changes during HIFU ablation at various powers, ranging from 10 to 130 W. The normalized mean square error of the proposed method is 0.28, approximately 2.15 on previous related methods.

Conclusion

These results demonstrated that this novel proposed approach, including the combination of sparsity and Shanoon's entropy, is more feasible and effective in temperature change estimation than its predecessors.

Uterine Ultrasound Doppler Hemodynamics of Magnesium Sulfate Combined with Labetalol in the Treatment of Pregnancy-Induced Hypertension Using Empirical Wavelet Transform Algorithm

The aim of this study was to explore the hemodynamic changes of magnesium sulfate combined with labetalol in the treatment of pregnancy-induced hypertension (PIH) under Doppler uterine ultrasound based on the empirical wavelet transform (EWT) algorithm. 500 patients with PIH in the hospital were selected and randomly divided into the control group (n = 250) and the observation group (n = 250). The control group was treated with conventional magnesium sulfate; the observation group was given labetalol based on magnesium sulfate drip in the control group. The uterine artery blood flow simulation model was established based on the EWT algorithm and compared with a short-time Fourier transform (STFT). The normalized root mean square error (NRMSE) of the STFT method was 0.19, and the NRMSE extracted by the EWT method was 0.13. After treatment, the blood pressure index, 24-hour urinary protein, and incidence of adverse birth outcomes in the observation group were lower than those in the control group; the effective rate of the control group (90.4%) was lower than that of the observation group (97.6%); the hemodynamic indexes of the uterine artery in the observation group were lower than those in the control group, and the differences were statistically significant (P < 0.05). The estimation accuracy of the EWT method was higher than that of the traditional STFT method; the combined treatment of magnesium sulfate and labetalol in patients with PIH had a remarkable effect, which could control the blood pressure index and reduce the 24-hour urinary protein; the uterine artery Doppler ultrasound examination could change hemodynamics and improve the adverse outcomes of mothers and infants.