Algorithms and results of eye tissues differentiation based on RF ultrasound

Quantitative evaluation of liver fibrosis based on ultrasound radio frequency signals: An animal experimental study

BACKGROUND

Chronic liver disease is an important cause of liver failure and death worldwide, and liver fibrosis is a common pathological process of most chronic liver diseases. There still lacks a useful tool for evaluating liver fibrosis progression precisely and non-invasively. The purpose of this study was to explore the use of ultrasound radio frequency (RF) signals combined with deep learning approach to evaluate the degree of liver fibrosis quantitatively.

METHODS

In this study, by extracting the output of deep learning models as a prediction value, a quantitative liver fibrosis prediction method was achieved based on the bidirectional long short-term memory (Bi-LSTM) network to analyze radio frequency (RF) signals. The dataset consisted of 160 sets of ultrasound RF signals of rat livers, including five fibrosis stages 0-4, upon pathological diagnosis. In total, 150 sets of RF signals were used to train four deep learning classification models, the output of which contained quantitative information. In each training stage of the four models, a large number of signal segments were extracted from the 150 sets and divided randomly into training and validation sets in a ratio of 80:20. Ten sets of RF data using the gold standard of quantitative fibrosis parameter (q-FP) of liver tissues were left for independent testing. To validate the proposed method, correlation analysis was carried out between q-FP and the quantitative prediction results based on the independent test data.

RESULTS

The accuracy of the four deep learning networks using the training and validation data was above 0.83 and 0.80, and the corresponding areas under the receiver operating characteristic curves were higher than 0.95 and 0.93, respectively. For the quantitative analysis in the independent test set, the determination coefficient, R², of the linear regression analysis between the quantitative prediction results and q-FP was above 0.93. liver fibrosis is a common pathological process of most chronic liver diseases.

CONCLUSIONS

This study indicates that a prediction system based on ultrasound RF signals and a deep learning approach is promising for realizing quantitative and visualized diagnosis of liver fibrosis, which would be of great value in monitoring liver fibrosis non-invasively.

Multidimensional spectral analysis of the ultrasonic radiofrequency signal for characterization of media

The importance of the analysis of the radiofrequency signal is by now recognized in the field of tissue characterization via ultrasound. The RF signal contains a wealth of information and structural details that are usually lost in the B-Mode representation. The HyperSPACE (Hyper SPectral Analysis for Characterization in Echography) algorithm presented by the authors in previous papers for clinical applications is based on the radiofrequency ultrasonic signal. The present work describes the method in detail and evaluates its performance in a repeatable and standardized manner, by using two test objects: a commercial test object that simulates the human parenchyma, and a laboratory-made test object consisting of human blood at different dilution values. In particular, the sensitivity and specificity in discriminating different density levels were estimated. In addition, the robustness of the algorithm with respect to the signal-to-noise ratio was also evaluated.

Electromagnetic limits to radiofrequency (RF) neuronal telemetry

The viability of a radiofrequency (RF) telemetry channel for reporting individual neuron activity wirelessly from an embedded antenna to an external receiver is determined. Comparing the power at the transmitting antenna required for the desired Channel Capacity, to the maximum power that this antenna can dissipate in the body without altering or damaging surrounding tissue reveals the severe penalty incurred by miniaturization of the antenna. Using both Specific Absorption Rate (SAR) and thermal damage limits as constraints, and 300 Kbps as the required capacity for telemetry streams 100 ms in duration, the model shows that conventional antennas smaller than 0.1 mm could not support human neuronal telemetry to a remote receiver (1 m away.) Reducing the antenna to 10 microns in size to enable the monitoring of single human neuron signals to a receiver at the surface of the head would require operating with a channel capacity of only 0.3 bps.