Ultrasonic Radio-frequency Echo Signals Research Articles

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

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. 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. 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. 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.

Classification of red blood cell aggregation using empirical wavelet transform analysis of ultrasonic radiofrequency echo signals

Grading red blood cell (RBC) aggregation is important for the early diagnosis and prevention of related diseases such as ischemic cardio-cerebrovascular disease, type II diabetes, deep vein thrombosis, and sickle cell disease. In this study, a machine learning technique based on an adaptive analysis of ultrasonic radiofrequency (RF) echo signals in blood is proposed, and its feasibility for classifying RBC aggregation is explored. Using an adaptive empirical wavelet transform (EWT) analysis, the ultrasonic RF signals are decomposed into a series of empirical mode functions (EMFs); then, dominant empirical mode functions (DEMFs) are selected from the series. Six statistical characteristics, including the mean, variance, median, kurtosis, root mean square (RMS), and skewness are calculated for the locally normalized DEMFs, aiming to form primary feature vectors. Random forest (RDF) and support vector machine (SVM) classifiers are trained with the given feature vectors to obtain prediction models for RBC classification. Ultrasonic RF echo signals are acquired from five groups of six types of porcine blood samples with average numbers of aggregated RBCs of 1.04, 1.20, 1.83, 2.31, 2.72, and 4.28, respectively, to test the classification performance of the proposed method. The best subset with regard to the variance, kurtosis, and RMS is determined according to the maximum accuracy based on the RDF and SVM classifiers. The classification accuracies are 84.03 ± 3.13% for the RDF classifier, and 85.88 ± 2.99% for the SVM classifier. The mean classification accuracy of the SVM classifier is 1.85% better than that of the RDF classifier. In conclusion, the machine learning method is useful for the discrimination of varying degrees of RBC aggregation, and has potential for use in characterizing and monitoring the RBC aggregation in vessels.

Adaptive Ultrasound Tissue Harmonic Imaging Based on an Improved Ensemble Empirical Mode Decomposition Algorithm

Complete and accurate separation of harmonic components from the ultrasonic radio frequency (RF) echo signals is essential to improve the quality of harmonic imaging. There are limitations in the existing two commonly used separation methods, that is, the subjectivity for the high-pass filtering (S_HPF) method and motion artifacts for the pulse inversion (S_PI) method. A novel separation method called S_CEEMDAN, based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, is proposed to adaptively separate the second harmonic components for ultrasound tissue harmonic imaging. First, the ensemble size of the CEEMDAN algorithm is calculated adaptively according to the standard deviation of the added white noise. A set of intrinsic mode functions (IMFs) is then obtained by the CEEMDAN algorithm from the ultrasonic RF echo signals. According to the IMF spectra, the IMFs that contain both fundamental and harmonic components are further decomposed. The separation process is performed until all the obtained IMFs have been divided into either fundamental or harmonic categories. Finally, the fundamental and harmonic RF echo signals are obtained from the accumulations of signals from these two categories, respectively. In simulation experiments based on CREANUIS, the S_CEEMDAN-based results are similar to the S_HPF-based results, but better than the S_PI-based results. For the dynamic carotid artery measurements, the contrasts, contrast-to-noise ratios (CNRs), and tissue-to-clutter ratios (TCRs) of the harmonic images based on the S_CEEMDAN are averagely increased by 31.43% and 50.82%, 18.96% and 10.83%, as well as 34.23% and 44.18%, respectively, compared with those based on the S_HPF and S_PI methods. In conclusion, the S_CEEMDAN method provides improved harmonic images owing to its good adaptivity and lower motion artifacts, and is thus a potential alternative to the current methods for ultrasonic harmonic imaging.

Ultrasound speed and attenuation in progressive trypsin digested articular cartilage

Subtle changes of articular cartilage (AC) can lead to tissue degeneration and even osteoarthritis (OA). The early degeneration of AC is closely related to a change in proteoglycans (PG) content. The observation of PG is therefore an appropriate way of studying OA and evaluating the degree of AC degeneration. In this study, 20 cartilage-bone samples were prepared from normal porcine femoral condyle cartilage and 10 samples were digested over 2 h using 0.25% trypsin solution. The dynamic process of PG-digestion was explored using a conventional A-mode ultrasound (US) experimental system with a 10 MHz center frequency. Quantitative acoustic parameters were calculated from ultrasonic radio-frequency echo signals and included US speed (USS), US amplitude attenuation coefficient (UAA) and broadband US attenuation coefficient (BUA). The experimental results showed that the conventional A-mode ultrasound is valuable for tracking the degree of PG-digestion. Histology also confirmed the validity of the ultrasound observations. For every AC sample, the degree of PG-digestion within a given time was different and was affected by individual differences. After two hours of degeneration, USS showed a mean decrease of 0.4% (P<0.05). UAA was significantly lower after a two-hour PG depletion period (from (2.45±0.23) to (2.28±0.41) dB mm⁻¹). BUA showed no significant differences during this process. In conclusion, conventional ultrasound can provide useful information about trypsin-induced progressive PG depletion in AC and can reflect variations of PG content via the quantitative acoustic parameters USS and UAA. The results of this study may be used to identify an indirect indicator of cartilage matrix integrity and OA disease progression.

Electronic and ultrasonic welding of plastics

Grading red blood cell (RBC) aggregation is important for the early diagnosis and prevention of related diseases such as ischemic cardio-cerebrovascular disease, type II diabetes, deep vein thrombosis, and sickle cell disease. In this study, a machine learning technique based on an adaptive analysis of ultrasonic radiofrequency (RF) echo signals in blood is proposed, and its feasibility for classifying RBC aggregation is explored. Using an adaptive empirical wavelet transform (EWT) analysis, the ultrasonic RF signals are decomposed into a series of empirical mode functions (EMFs); then, dominant empirical mode functions (DEMFs) are selected from the series. Six statistical characteristics, including the mean, variance, median, kurtosis, root mean square (RMS), and skewness are calculated for the locally normalized DEMFs, aiming to form primary feature vectors. Random forest (RDF) and support vector machine (SVM) classifiers are trained with the given feature vectors to obtain prediction models for RBC classification. Ultrasonic RF echo signals are acquired from five groups of six types of porcine blood samples with average numbers of aggregated RBCs of 1.04, 1.20, 1.83, 2.31, 2.72, and 4.28, respectively, to test the classification performance of the proposed method. The best subset with regard to the variance, kurtosis, and RMS is determined according to the maximum accuracy based on the RDF and SVM classifiers. The classification accuracies are 84.03 ± 3.13% for the RDF classifier, and 85.88 ± 2.99% for the SVM classifier. The mean classification accuracy of the SVM classifier is 1.85% better than that of the RDF classifier. In conclusion, the machine learning method is useful for the discrimination of varying degrees of RBC aggregation, and has potential for use in characterizing and monitoring the RBC aggregation in vessels.