Background—Atherosclerotic plaque stability is related to histological composition. However, current diagnostic tools do not allow adequate in vivo identification and characterization of plaques. Spectral analysis of backscattered intravascular ultrasound (IVUS) data has potential for real-time in vivo plaque classification. Methods and Results—Eighty-eight plaques from 51 left anterior descending coronary arteries were imaged ex vivo at physiological pressure with the use of 30-MHz IVUS transducers. After IVUS imaging, the arteries were pressure-fixed and corresponding histology was collected in matched images. Regions of interest, selected from histology, were 101 fibrous, 56 fibrolipidic, 50 calcified, and 70 calcified-necrotic regions. Classification schemes for model building were computed for autoregressive and classic Fourier spectra by using 75% of the data. The remaining data were used for validation. Autoregressive classification schemes performed better than those from classic Fourier spectra with accuracies of 90.4% for fibrous, 92.8% for fibrolipidic, 90.9% for calcified, and 89.5% for calcified-necrotic regions in the training data set and 79.7%, 81.2%, 92.8%, and 85.5% in the test data, respectively. Tissue maps were reconstructed with the use of accurate predictions of plaque composition from the autoregressive classification scheme. Conclusions—Coronary plaque composition can be predicted through the use of IVUS radiofrequency data analysis. Autoregressive classification schemes performed better than classic Fourier methods. These techniques allow real-time analysis of IVUS data, enabling in vivo plaque characterization. (Circulation. 2002;106:2200-2206.)

Coronary Plaque Classification With Intravascular Ultrasound Radiofrequency Data Analysis

Due to parallel advances in signal processing and computer hardware in the last 15 years, quantitative ultrasound techniques have reached maturity, allowing for the construction of quantitative maps or images of soft tissues. This book will focus on 5 modern research topics related to quantitative ultrasound of soft tissues: - Spectral-based methods for tissue characterization, tissue typing, cancer detection, etc.; - Envelope statistics analysis as a means of quantifying and imaging tissue properties; - Ultrasound elastography for quantifying elastic properties of tissues (several clinical ultrasound scanners now display elastography images); - Scanning acoustic microscopy for forming images of mechanical properties of soft tissues with micron resolution (desktop size scanners are now available); and - Ultrasound computer tomography for breast cancer imaging (new ultrasound tomography systems have been developed and are currently under evaluation clinically).

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Quantitative Ultrasound in Soft Tissues

A system and method is provided for using backscattered data and known parameters to characterize vascular tissue. Specifically, in one embodiment of the present invention, an ultrasonic device is used to acquire RF backscattered data (i.e., IVUS data) from a blood vessel. The IVUS data is then transmitted to a computing device and used to create an IVUS image. The blood vessel is then cross-sectioned and used to identify its tissue type and to create a corresponding image (i.e., histology image). A region of interest (ROI), preferably corresponding to the identified tissue type, is then identified on the histology image. The computing device, or more particularly, a characterization application operating thereon, is then adapted to identify a corresponding region on the IVUS image. To accurately match the ROI, however, it may be necessary to warp or morph the histology image to substantially fit the contour of the IVUS image. After the corresponding region is identified, the IVUS data that corresponds to this region is identified. Signal processing is then performed and at least one parameter is identified. The identified parameter and the tissue type (e.g., characterization data) is stored in a database. In another embodiment of the present invention, the characterization application is adapted to receive IVUS data, determine parameters related thereto (either directly or indirectly), and use the parameters stored in the database to identify a tissue type or a characterization thereof.

System and method of characterizing vascular tissue

Autoregressive (AR) models are qualified for analysis of stochastic, short-time data, such as intravascular ultrasound (IVUS) backscatter. Regularization is required for AR analysis of short data lengths with an aim to increase spatial accuracy of predicted plaque composition and was achieved by determining suitable AR orders for short data records. Conventional methods of determining order were compared to the use of trend in the mean square error for determining order. Radio-frequency data from 101 fibrous, 56 fibro-lipidic, 50 calcified, and 70 lipid-core regions of interest (ROIs) were collected ex vivo from 51 human coronary arteries with 30 MHz unfocused IVUS transducers. Spectra were computed for AR model orders between 3-20 for data representing ROIs of two sizes (32 and 16 samples at 100 MHz sampling frequency) and were analyzed in the 17-42 MHz bandwidth. These spectra were characterized based on eight previously identified parameters. Statistical classification schemes were computed from 75% of the data and cross-validated with the remaining 25% using matched histology. The results determined the suitable AR order numbers for the two ROI sizes. Conventional methods of determining order did not perform well. Trend in the mean square error was identified as the most suitable factor for regularization of short record lengths.

Regularized autoregressive analysis of intravascular ultrasound backscatter: improvement in spatial accuracy of tissue maps

The statistics of envelope of high-frequency ultrasonic backscatter signals from in vivo normal human dermis and subcutaneous fat were studied. The capability of six probability distributions (Rayleigh, Rician, K, Nakagami, Weibull, and Generalized Gamma) to model empirical envelope data was studied using the Kolmogorov-Smirnov (KS) goodness of fit statistic. The parameters of all the distributions were obtained using the maximum likelihood method. It was found that the Generalized Gamma distribution with two shape parameters provided the best fit among all the distributions in terms of the KS goodness of fit. The K and Weibull distributions also modeled the envelope statistics well. The Rayleigh and Rician distributions provided poorer fits. The parameters of the Generalized Gamma distribution, however, showed a larger variability than those of the other distributions. The intersubject variability in the estimated parameters of all the distributions was found to be comparable to the intrasubject variability. Fat was seen to exhibit significantly more pre-Rayleigh behavior compared to the dermis. The parameters of the Generalized Gamma distribution also showed significant differences between the dermis at the forearm and fingertip regions.

Statistics of envelope of high-frequency ultrasonic backscatter from human skin in vivo

The authors deal with the application of parametric spectral analysis for attenuation estimation on the reflected ultrasound signal of biological tissues. A second-order autoregressive (AR2) model, whose parameters are estimated with the Burg algorithm, is used to estimate the center frequency on echo signals and its evolution versus depth. Data simulation of independent A-lines backscattered by a homogeneous medium of scatterers are generated by a computer model with attenuation values ranging from 1 to 5 dB/cmMHz, an ultrasonic frequency of 5 MHz and different pulse durations. The performance of the estimator is evaluated for time windows ranging from 5 to 0.3 /spl mu/s. The comparison is made with the classical short time Fourier analysis using a fast Fourier transform (FFT). It is found that the AR2 model provides a better estimation of attenuation than the Fourier technique: the relative error of attenuation is below 5% for windows between 0.6 to 2.5 /spl mu/s, while the one obtained with the Fourier technique lies between 3 and 16% for the same window sizes. However, the variance of attenuation estimate is the same with the two techniques. These results offer promises for determining attenuation in highly attenuating medium (material or biological tissue) either because of their structure or because high frequencies are used. >

Application of autoregressive spectral analysis for ultrasound attenuation estimation: interest in highly attenuating medium

The frequency-dependent attenuation is an interesting acoustic parameter for tissue characterization. For in vivo skin characterization, the anatomy of this biological tissue requires an echographic technique since transmission techniques are not available. In case of highly attenuating medium such as skin, limitations due to the method (stationarity, window size) prevent the use of Fourier analysis. Therefore, the authors have developed a new technique, based on the "centroid algorithm" using auto-regressive modeling. It allows a quasi-local mean-frequency estimation. Preliminary experimental results obtained at 20 MHz on porcine skin in vitro are shown. Acquisition in double transmission allows the measurement of the frequency dependence of attenuation. From the echographic signal, conventional B-scan images show two different regions that have been histologically identified to be the dermis and the hypodermis, respectively. Variations of the attenuation coefficient in the different skin layers (dermis, hypodermis) are detected and are reproducible on a set of nine skin samples. This study shows the feasibility of characterizing layered media using centroid and attenuation profiles.

An in vitro study on porcine skin: attenuation profile estimation using auto-regressive modeling

A second-order autoregressive (AR2) model, whose parameters are estimated with the Burg algorithm, is used to estimate the center-frequency along echo signals and its evolution versus depth. Data simulation of independent A-lines reflected by a homogeneous medium of scatterers are generated by a computer model with attenuation values ranging from 1 to 5 dB/cm MHz and an ultrasonic frequency of 5 MHz. The performance of the estimator is evaluated for duration of time windows ranging from 5.12 to 0.32 ms and different spectral sampling. The comparison is made with the classical Fourier spectrogram technique (FFT). It is found that the AR model provides a better estimation of attenuation than the Fourier technique: the relative error of attenuation is below 5% for windows between 0.64 to 2.56 ms, while the one obtained with the Fourier technique lies between 10 and 70% for the same window sizes. These results offer promises for determining attenuation in biological medium which are highly attenuating either because of their structure, like bone, or because high frequencies are used

Application of autoregressive spectral analysis for ultrasound attenuation: interest in highly attenuating medium

The recent introduction of high frequency ultrasound in dermatology has opened new perspectives concerning skin architecture studies and skin diagnosis in dermatological disease. 25-MHz ultrasound technology is now widely available and has proven to be useful, particularly to define the dimensions of skin tumours [7, 8, 10]. In this case, ultrasound used in a traditional B-scan imaging provides a valuable clinical tool.

Ultrasound Attenuation Estimation in Highly Attenuating Media: Application to Skin Characterization

Attenuation estimation from the ultrasound signal backscattered by biological tissues with the lcentroid algorithmr requires a mean frequency estimator. We previously presented the second-order auto-regressive (AR2) model for mean frequency and attenuation estimation. In this paper, we focus on a new mean frequency estimator based on an approximation of the first derivative of the autocorrelation function of the demodulated echographic signal at the origin, first introduced for Doppler signal analysis. This adapted estimator (AE) is compared with the AR2 modeling and with the classical Fourier spectrogram technique (FFT) in case of simulated echographic signals using a 20 MHz frequency probe and experimental signals obtained from a scattering phantom with calibrated attenuation. This study shows that the AE and the AR2 estimators lead to a better attenuation estimation than the FFT technique. First in vitro results on porcine skin are shown. These results offer promises for determining attenuation in highly attenuating media either because of their structure or because high frequencies are used

T. Baldeweck

Papers

Application of autoregressive spectral analysis for ultrasound attenuation estimation: interest in highly attenuating medium

An in vitro study on porcine skin: attenuation profile estimation using auto-regressive modeling

Application of autoregressive spectral analysis for ultrasound attenuation: interest in highly attenuating medium

Ultrasound Attenuation Estimation in Highly Attenuating Media: Application to Skin Characterization

Attenuation estimation in highly attenuating media using high frequencies: a comparison study between different mean frequency estimators