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In vivo plaque characterization is an important research field in interventional cardiology. We will study the realistic challenges to this goal by deploying 40 MHz single-element, mechanically rotating transducers. The intrinsic variability among the transducers' spectral parameters as well as tissue signals will be demonstrated. Subsequently, we will show that global data normalization is not suited for data calibration, due to the aforementioned variations as well as the stringent characteristics of spectral features. We will describe the sensitivity of an existing feature extraction algorithm based on eight spectral signatures (integrated backscatter coefficient, slope, midband-fit (MBF), intercept, and maximum and minimum powers and their relative frequencies) to a number of factors, such as the window size and order of the autoregressive (AR) model. It will be further demonstrated that the variations in the transducer's spectral parameters (i.e., center frequency and bandwidth) cause inconsistencies among extracted features. In this paper, two fundamental questions are addressed: 1) what is the best reliable way to extract the most informative features? and 2) which classification algorithm is the most appropriate for this problem? We will present a full-spectrum analysis as an alternative to the eight-feature approach. For the first time, different classification algorithms, such as k-nearest neighbors (k-NN) and linear Fisher, will be employed and their performances quantified. Finally, we will explore the reliability of the training dataset and the complexity of the recognition algorithm and illustrate that these two aspects can highly impact the accuracy of the end result, which has not been considered until now.

Plaque characterization through backscattered intravascular ultrasound (IVUS) signal analysis has been the subject of extensive study for the past several years. A number of algorithms to analyze IVUS images and underlying RF signals to delineate the composition of atherosclerotic plaque have been reported. In this paper, we present several realistic challenges one faces throughout the process of developing such algorithms to characterize tissue type.The basic tenet of ultrasound tissue characterization is that different tissue types imprint their own "signature" on the backscattered echo returning to the transducer. Tissue characterization is possible to the extent that these echo signals can be received, the signatures read, and uniquely attributed to a tissue type. The principal difficulty in doing tissue characterization is that backscattered RF signals originating as echoes from different groups of cells of the same tissue type exhibit no obvious commonality in appearance in the time domain. This happens even in carefully controlled laboratory experiments.We describe the method of acquisition and digitization of ultrasound radiofrequency (RF) signals from left anterior descending and left circumflex coronary arteries. The challenge of obtaining corresponding histology images to match to specific regions-of-interest on the images is discussed.A tissue characterization technique based on seven features is compared to a full spectrum based approach. The same RF and histology data sets were used to evaluate the performances of these two techniques.

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This preliminary study showed that the novel fully automated tracing system based on the multifrequency processing algorithm can provide more accurate lumen border detection than current automated tracing systems and thus, offer a more reliable quantitative evaluation of lumen geometry.