RF Time Series Research Articles

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.

Characterisation of the effects of age and body mass index on the ultrasound carotid artery wall using RF time series

ABSTRACTCarotid atherosclerosis and brain strokes are mainly due to plaques and artery wall stiffness, therefore, novel methods are required to investigate such diseases at initial stages. Many Studies, using radiofrequency signals, have indicated changes in artery wall due to ageing and body mass index in healthy people. In this study, a novel method that uses power spectrum and Nakagami distribution methods based on radiofrequency signals evaluates changes of acoustic and statistical features of carotid artery wall. The posterior wall (intima-media) was extracted for 31 healthy participants including 19 men and 12 women (with age distribution of ) using their radiofrequency signals. The results showed that the power spectrum parameters correlate with age. The quantitative analysis indicated that power spectrum parameters decrease along frames by age. In addition, scaling and Nakagami parameters showed significant correlations with age () and body mass index () respectively. Nakagami distribution parameters increase by age and are higher in overweight participants. However, there were no significant correlations between these features and systolic/diastolic blood pressure and sex. Our Results raise hopes that the proposed method may be effective in diagnosing of atherosclerosis.

Ultrasonic RF Time Series for Early Assessment of Tumor Response to Chemotherapy: First in Vivo Studies on Mice Breast Cancer Model

Ultrasonic RF Time Series for Early Assessment of Tumor Response to Chemotherapy: First in Vivo Studies on Mice Breast Cancer Model

Computer-Aided Prostate Cancer Detection Using Ultrasound RF Time Series: In Vivo Feasibility Study.

This paper presents the results of a computer-aided intervention solution to demonstrate the application of RF time series for characterization of prostate cancer, in vivo. We pre-process RF time series features extracted from 14 patients using hierarchical clustering to remove possible outliers. Then, we demonstrate that the mean central frequency and wavelet features extracted from a group of patients can be used to build a nonlinear classifier which can be applied successfully to differentiate between cancerous and normal tissue regions of an unseen patient. In a cross-validation strategy, we show an average area under receiver operating characteristic curve (AUC) of 0.93 and classification accuracy of 80%. To validate our results, we present a detailed ultrasound to histology registration framework. Ultrasound RF time series results in differentiation of cancerous and normal tissue with high AUC.

Ultrasound-Based Characterization of Prostate Cancer Using Joint Independent Component Analysis.

This paper presents the results of a new approach for selection of RF time series features based on joint independent component analysis for in vivo characterization of prostate cancer. We project three sets of RF time series features extracted from the spectrum, fractal dimension, and the wavelet transform of the ultrasound RF data on a space spanned by five joint independent components. Then, we demonstrate that the obtained mixing coefficients from a group of patients can be used to train a classifier, which can be applied to characterize cancerous regions of a test patient. In a leave-one-patient-out cross validation, an area under receiver operating characteristic curve of 0.93 and classification accuracy of 84% are achieved. Ultrasound RF time series can be used to accurately characterize prostate cancer, in vivo without the need for exhaustive search in the feature space. We use joint independent component analysis for systematic fusion of multiple sets of RF time series features, within a machine learning framework, to characterize PCa in an in vivo study.

Ultrasound-Guided Characterization of Interstitial Ablated Tissue Using RF Time Series: Feasibility Study

This paper presents the results of a feasibility study to demonstrate the application of ultrasound RF time series imaging to accurately differentiate ablated and nonablated tissue. For 12 ex vivo and two in situ tissue samples, RF ultrasound signals are acquired prior to, and following, high-intensity ultrasound ablation. Spatial and temporal features of these signals are used to characterize ablated and nonablated tissue in a supervised-learning framework. In cross-validation evaluation, a subset of four features extracted from RF time series produce a classification accuracy of 84.5%, an area under ROC curve of 0.91 for ex vivo data, and an accuracy of 85% for in situ data. Ultrasound RF time series is a promising approach for characterizing ablated tissue.

Augmenting Detection of Prostate Cancer in Transrectal Ultrasound Images Using SVM and RF Time Series

We propose a novel and accurate method based on ultrasound RF time series analysis and an extended version of support vector machine classification for generating probabilistic cancer maps that can augment ultrasound images of prostate and enhance the biopsy process. To form the RF time series, we record sequential ultrasound RF echoes backscattered from tissue while the imaging probe and the tissue are stationary in position. We show that RF time series acquired from agar-gelatin-based tissue mimicking phantoms, with difference only in the size of cell-mimicking microscopic glass beads, are distinguishable with statistically reliable accuracies up to 80.5%. This fact indicates that the differences in tissue microstructures affect the ultrasound RF time series features. Based on this phenomenon, in an ex vivo study involving 35 prostate specimens, we show that the features extracted from RF time series are significantly more accurate and sensitive compared to two other established categories of ultrasound-based tissue typing methods. We report an area under receiver operating characteristic curve of 0.95 in tenfold cross validation and 0.82 in leave-one-patient-out cross validation for detection of prostate cancer.