Noninvasive calibrated tissue temperature estimation using backscattered energy of acoustic harmonics

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.

A Data-Driven Approach for Estimating Temperature Variations Based on B-mode Ultrasound Images and Changes in Backscattered Energy

Thermal treatments that use ultrasound devices as a tool have as a key point the temperature control to be applied in a specific region of the patient's body. This kind of procedure requires caution because the wrong regulation can either limit the treatment or aggravate an existing injury. Therefore, determining the temperature in a region of interest in real-time is a subject of high interest. Although this is still an open problem, in the field of ultrasound analysis, the use of machine learning as a tool for both imaging and automated diagnostics are application trends. In this work, a data-driven approach is proposed to address the problem of estimating the temperature in regions of a B-mode ultrasound image as a supervised learning problem. The proposal consists in presenting a novel data modeling for the problem that includes information retrieved from conventional B-mode ultrasound images and a parametric image built based on changes in backscattered energy (CBE). Then, we compare the performance of classic models in the literature. The computational results presented that, in a simulated scenario, the proposed approach that a Gradient Boosting model would be able to estimate the temperature with a mean absolute error of around 0.5°C, which is acceptable in practical environments both in physiotherapic treatments and high intensity focused ultrasound (HIFU).

Interdependence of Tissue Temperature, Cavitation, and Displacement Imaging During Focused Ultrasound Nerve Sonication

Focused ultrasound (FUS) peripheral neuromodulation has been linked to nerve displacement caused by the acoustic radiation force; however, the roles of cavitation and temperature accumulation on nerve modulation are less clear, as are the relationships between these three mechanisms of action. Temperature directly changes tissue stiffness and viscosity. Viscoelastic properties have been shown to affect cavitation thresholds in both theoretical and ex vivo models, but the direct effect of temperature on cavitation has not been investigated in vivo. Here, cavitation and tissue displacement were simultaneously mapped in response to baseline tissue temperatures of either 30 °C or 38 °C during sciatic nerve sonication in mice. In each mouse, the sciatic nerve was repeatedly sonicated at 1.1-MHz, 4-MPa peak-negative pressure, 5-ms pulse duration, and either 15- or 30-Hz pulse repetition frequency (PRF) for 10 s at each tissue temperature. Cavitation increased by 1.8-4.5 dB at a tissue temperature of 38 °C compared to 30 °C, as measured both by passive cavitation images and cavitation doses. Tissue displacement also increased by 1.3- [Formula: see text] at baseline temperatures of 38 °C compared to 30 °C. Histological findings indicated small increases in red blood cell extravasation in the 38 °C baseline temperature condition compared to 30 °C at both PRFs. A strong positive correlation was found between the inertial cavitation dose and displacement imaging noise, indicating the potential ability of displacement imaging to simultaneously detect inertial cavitation in vivo. Overall, tissue temperature was found to modulate both in vivo cavitation and tissue displacement, and thus, both tissue temperature and cavitation can be monitored during FUS to ensure both safety and efficiency.

Decorrelated compounding of synthetic aperture ultrasound imaging to detect low contrast thermal lesions induced by focused ultrasound

PURPOSE

Decorrelated Compounding (DC) for synthetic aperture ultrasound can reduce speckle variation in images, suggesting enhanced detectability of low-contrast targets in tissue including thermal lesions produced by focused ultrasound (FUS). The DC imaging method has primarily been investigated in simulation and in phantom studies. This work investigates the feasibility of the DC method in monitoring thermal therapy via image guidance and non-invasive thermometry based on the change in backscattered energy (CBE).

METHODS

Ex vivo porcine tissue was exposed to FUS exposures at acoustic powers of 5 W and 1 W, with peak pressure amplitudes of 0.64 MPa and 0.27 MPa respectively. During FUS exposure, RF echo data frames was acquired using a 7.8 MHz linear array probe and a Verasonics Vantage^TM ultrasound scanner (Verasonics Inc., Redmond, WA). RF echo data was taken to produce B-mode images, as reference images. Synthetic aperture RF echo data was also acquired and processed using delay-and-sum (DAS), a combination of spatial and frequency compounding referred to as Traditional Compounding (TC), and the proposed DC imaging methods. Image quality was assessed using the contrast-to-noise ratio (CNR) at the FUS beam focus, and the speckle SNR (sSNR) of the background region as preliminary metrics. A calibrated thermocouple was placed near the FUS beam focus for temperature measurements and calibrations using the CBE method.

RESULTS

The DC imaging method significantly improved image quality to detect low contrast thermal lesions in treated ex vivo porcine tissue in comparison to other imaging methods. In comparison to B-mode imaging, the lesion CNR measured using the DC imaging was shown to improve up to a factor of approximately 5.5. The corresponding sSNR improved by a factor of approximately 4.2 in comparison to B-mode imaging. CBE calculation using the DC imaging method yielded more precise measurements of the backscattered energy compared to other imaging methods studied.

CONCLUSIONS

The despeckling performance of the DC imaging method significantly improves the lesion CNR in comparison to B-mode imaging. This suggests that the proposed method can detect low-contrast thermal lesions induced by FUS therapy that are not detectable using standard B-mode imaging. Furthermore, the signal change at the focal point were more precisely measured by DC imaging, and the signal change in response to FUS exposure follows the temperature profile more closely than changes measured using B-mode, as well as synthetic aperture DAS and TC images. These suggest that DC imaging can potentially be used with the CBE method to improve non-invasive thermometry.

Thermometry using entropy imaging of ultrasound radio frequency signal time series

Low intensity focused ultrasound (LIFU) is a novel approach that could activate drug release and considerably improve the delivery of anticancer drug. LIFU treatment has some features like is able to penetrate deep into the tissue and being non-invasive, as a consequence LIFU displays great capability for controlling the drug release and improving the chemotherapy treatment efficiency. The goal of this study is to research the feasibility of the entropy parameter of RF time series of ultrasound backscattered signals for measuring the changes in temperature induced by a LIFU device. Entropy Imaging is a technique for reconstructing ultrasound images based on the average uncertainty of time-series in a signal. Furthermore, the Shannon Entropy can quantify the uncertainty of a random process and is usually used as a measure for the information content of probability distributions. In this study, we use the Entropy Imaging method for measuring the LIFU-induced temperature changes in the deep region of ex vivo porcine tissue samples. The results obtained show that the changes of entropy parameter of RF time series signal are proportional to temperature changes recorded by a calibrated thermocouple in the temperature range of 37–47°C. In conclusion, in this study we show that Shannon entropy of RF time series signal possesses promising features like succinctly capturing the available information in a system by considering the uncertainty in a given data that can be used, as a new method, to measure temperature changes non-invasively and quantitatively in the deep region of tissue.