Radiofrequency Echo Signals Research Articles

New and future developments in ultrasonic imaging.

In the first part of the review, recent developments in medical imaging technology are described. Developments in transducer materials and matching, leading to improvements in band-width and sensitivity are discussed. Improvements in dynamic range due to increased transducer sensitivity, lower electronic noise levels and more efficient filtering are then considered. The benefits of the application of digital signal processing (DSP) techniques to radiofrequency (RF) echo signals are described, including more precise filtering and beam forming, synthetic aperture and parallel receive beam forming. Finally, the current situation in regard to 1.5 D arrays, 3 D scanning, ultrasound computed tomography (UCT), harmonic imaging with contrast agents and elastography are discussed. In the second part, some predictions for future developments are made. These will be possible largely due to the power of DSP. Parallel transmissions will make more efficient use of time, allowing greater spatial and temporal resolution, and greater accuracy in Doppler imaging. Adaptive transmission tailoring will be used, where the pulse characteristics to each part of the image field are independently optimized, as will adaptive receive processing in which echo sequences from each part of the image are independently and optimally processed. An important potential development will be automatic feature recognition, making possible accurate compound scanning with high spatial resolution, and quantitative information about the spatial distribution of acoustic speed. Compound scanning will provide more complete visualization of all structures and, particularly when incorporated into intravascular probes, should greatly aid the investigation of arterial plaque morphology. Feature recognition will also make it possible to have UCT systems (array based in future) which require less than 360 degrees access. Harmonic imaging without contrast agents, based simply on the inherent non-linearity of sound propagation in tissue, will become common. 2 D phased array transducer will permit symmetric beam focusing and scanning throughout a solid cone, greatly facilitating the development of 3 D scanning applications. Large 2 D arrays would have the potential to produce a five-fold increase in spatial resolution of a limited volume of tissue, or to measure the variation of backscatter with angle, as an aid to tissue characterization. Finally, ultrasound will be increasingly used to measure the elastic and dynamic properties of local regions of tissue.

Reconstruction of elastic modulus distribution from envelope detected B-mode data

A variety of ultrasonic methods have been shown to be capable of imaging the stiffness of soft tissue but most require specialised equipment and non-standard approaches to ultrasonic examination. In breast imaging by Elastography, for example, the elastic modulus distribution (EMD) is estimated by using 1-D cross correlation tracking of radiofrequency (RF) echo signals to image the distribution of strain induced by a controlled static deformation. Our work aims to explore the possibilities for employing standard ultrasound scanning methods and equipment, and simple processing techniques, to image the EMD of soft tissues. A simple reconstruction algorithm was developed and its convergence was studied at different elastic contrasts (EC) using 'ideal' strain data that was derived using the finite element method. EMD reconstruction of hard inclusions improved substantially according to a Chi-squared statistic, but soft inclusions showed little change after the first iteration. For example Chi-squared for a +20 dB EC inclusion reduced from 10 at the start to 0.0001 after 5 iterations, whilst it improved from 3 to only 1.6 for a -20 dB inclusion. The performance of a 1-D cross correlation tracking algorithm was studied with RF and envelope detected data when external strains of 2% and 5% were applied to the surface of simulated phantom. The algorithm accurately estimated displacement /spl les/1.5/spl lambda/ with both RF and ED data when the applied strain was 2%. However better precision and signal to noise ratio (SNR) was obtained using RF as oppose to ED. At an applied strain of 5% more accurate estimates, better precision and SNR was obtain using ED. We conclude that there is no great disadvantage to using the envelope detected signal for estimating internal tissue displacements that are likely to be encountered in standard clinical scanning practice.

Statistics of ultrasonic spectral parameters for prostate and liver examinations

A theoretical analysis was performed to describe statistical characteristics of calibrated spectral parameters used for ultrasonic tissue evaluation in the prostate and liver. The analysis assumes that radiofrequency (rf) echo signals exhibit Gaussian statistics. It derives the probability density function (pdf) of spectral parameters that are computed using sliding-window analysis techniques. The analysis relates the standard deviations of linear-regression spectral-parameter estimates to system and analysis parameters including bandwidth, center frequency, and the length of the sliding analysis window. The analysis also derives the pdf for mid-band fit parameter images. Theoretical results are found to agree well with clinical data from homogeneous segments in liver and prostate. The results offer a basis for evaluating spectral-estimator precision and for conducting future studies of lesion detectability based on spectral features.

Ultrasonic spectral-parameter imaging of the prostate

Spectrum analysis of the radiofrequency echo signals obtained from ultrasonically scanning the prostate may provide information capable of distinguishing cancerous from noncancerous tissue. In American men, prostate cancer is the highest-incidence cancer and the second-highest cancer killer. It is diagnosed using ultrasonically guided biopsies, which are limited by the low sensitivity and specificity of the guidance method. Spectrum analysis of the echo signals uses information that is discarded by conventional ultrasound imaging technology. The inclusion of this information shows differences between the ultrasound-scattering properties of cancerous and noncancerous prostate tissues. Spectrum analysis of ultrasonic echoes provides parameter values that can be related to scattering properties of tissue and can be compared to database parameter value ranges associated with cancerous and noncancerous tissues. Images can be generated to display parameter values, scatterer properties, or most likely tissue type. Results to date suggest that these differences may be sufficient to improve biopsy guidance significantly and therefore to improve the efficacy of biopsy-based diagnosis of prostate cancer. © 1997 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 8, 11–25, 1997

Statistical framework for ultrasonic spectral parameter imaging

This study examines the statistics of ultrasonic spectral parameter images that are being used to evaluate tissue microstructure in several organs. The parameters are derived from sliding-window spectrum analysis of radiofrequency echo signals. Calibrated spectra are expressed in dB and analyzed with linear regression procedures to compute spectral slope, intercept and midband fit, which is directly related to integrated backscatter. Local values of each parameter are quantitatively depicted in gray-scale cross-sectional images to determine tissue type, response to therapy and physical scatterer properties. In this report, we treat the statistics of each type of parameter image for statistically homogeneous scatterers. Probability density functions are derived for each parameter, and theoretical results are compared with corresponding histograms clinically measured in homogeneous tissue segments in the liver and prostate. Excellent agreement was found between theoretical density functions and data histograms for homogeneous tissue segments. Departures from theory are observed in heterogeneous tissue segments. The results demonstrate how the statistics of each spectral parameter and integrated backscatter are related to system and analysis parameters. These results are now being used to guide the design of system and analysis parameters, to improve assays of tissue heterogeneity and to evaluate the precision of estimating features associated with effective scatterer sizes and concentrations.

Typing of prostate tissue by ultrasonic spectrum analysis

Prostate cancer is the highest-incidence cancer and second-leading cancer killer of men in the U.S. Diagnosis now relies virtually exclusively on core-needle biopsy, guided by transrectal ultrasound (TRUS). Because of the limitations of TRUS in detecting suspicious regions, biopsy often fails to sample cancer that is present or to determine that extracapsular cancer exists, which results in false-negative biopsies or inappropriate prostatectomies. Therefore, the authors conducted this study to investigate the use of spectrum analysis of radio frequency (RF) echo signals as a possible means of reducing the number of false-negative biopsies and inappropriate prostatectomies. This method utilizes databases of parameters derived from normalized power spectra of RF echo signals and histologically proven tissue types to determine ranges of parameter values associated with tissue types of interest. Typing an unknown tissue is performed by comparing the parameter values of the unknown to the value ranges of specific tissue types in the database. The authors' results provide encouraging preliminary discriminant-function distributions that suggest an excellent potential for differentiating cancerous from noncancerous prostate tissue far superior in terms of sensitivity and specificity than means now used to determine whether biopsy is required. In addition, the authors developed images using color to indicate the most likely tissue type throughout the tissue cross section as determined by comparisons with database parameter values. These images showed excellent correlation with histology.

Effect of the beam-vessel angle on the received acoustic signal from blood

In order to explore the feasibility of algorithms to determine the three dimensional (3D) velocity magnitude from the received ultrasonic blood echo from a single line of sight, the signal from small sample volumes is studied as a function of beam-vessel angle. As opposed to previous treatments of the effect of the beam-vessel angle on the received acoustic signal, a wideband signal is transmitted and the returned signal in each sample volume is analyzed. High-resolution experimental M-mode images of radio-frequency (rf) echo signals are used to visualize the flow in individual regions of interest. These experiments confirm the predictions of a theoretical model for the signal and its second moment. It is shown that the two major effects limiting the correlated signal interval are the spread of axial velocities within the sample volume and the transit time across the lateral beam width. Particularly for small beam-vessel angles, the spread of velocities limits the correlated signal interval. In addition, the experimental results demonstrate that accurate velocity estimation for low volume flow rates and particularly for large beam-vessel angles may involve detection of changes in the correlation magnitude. For low volume flow rates, the shape of the correlation surface can be affected by small regions of blood with a strong scattering intensity located near the initial region of interest. >

Cellularity and fibrin mesh properties as a basis for ultrasonic tissue characterization of blood clots and thrombi

This in vitro study was designed to evaluate the ability of ultrasonic tissue characterization (UTC) based on power spectrum analysis of backscattered radio-frequency echo signals to distinguish two prominent variables of thrombi: cellularity (primarily red cell content) and fibrin-mesh density. Six types of clots simulating thrombus components were prepared by varying red-cell and platelet concentrations and shear forces during clotting. Data were acquired with a linear-array transducer, digitized, and analyzed in terms of slope and intercept parameters obtained from normalized power spectra of radio-frequency echo signals. Increased cellularity and fibrin-mesh density both produced lower slope and higher intercept values, which permitted statistically significant discrimination of cellularity and mesh density in the six types of clots analyzed. Shearing forces and (to a lesser degree) platelet concentrations increased fibrin-mesh density. This study suggests that UTC based upon the power spectrum of echo signals may be used to detect and follow compositional differences that have clinical relevance in the diagnosis and follow-up of thrombi.

Ultrasound attenuation and texture analysis of diffuse liver disease: methods and preliminary results

A study was performed to find and test quantitative methods of analysing echographic signals for the differentiation of diffuse liver diseases. An on-line data acquisition system was used to acquire radiofrequency (RF) echo signals from volunteers and patients. Several methods to estimate the frequency-dependent attenuation coefficient were evaluated, in which a correction for the frequency and depth-dependent diffraction and focusing effects caused by the sound beam was applied. Using the estimated value of the attenuation coefficient the RF signals themselves were corrected to remove the depth dependencies caused by the sound beam and by the frequency-dependent attenuation. After this preprocessing the envelope of the corrected RF signals was calculated and B-mode images were reconstructed. The texture was analysed in the axial direction by first- and second-order statistical methods. The accuracy and precision of the attenuation methods were assessed by using computer simulated RF signals and RF data obtained from a tissue-mimicking phantom. The phantom measurements were also used to test the performance of the methods to correct for the depth dependencies.

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.