Raw Radiofrequency Research Articles

Intravascular ultrasound tissue characterization. I like the rainbow but... what's behind the colours?

Intravascular ultrasound (IVUS) remains the gold standard for, precise, high-quality visualization of atherosclerotic plaque on the vessel wall.1 Conventional grey-scale IVUS provides unique insights into the underlying substrate of atherosclerotic disease, and classical studies have demonstrated that plaque echogenicity correlated with its histological composition. However, despite its ability to provide direct insight on plaque composition, grey-scale IVUS has been unable to identify vulnerable plaques.2 The reason for this appears to be multifaceted. First, the current resolution of the technique is unable to define some subtle characteristics of these plaques, including the thickness of the fibrous cap. Secondly, ‘thrombotic-prone’ plaques have several pathological substrates apart from the classical ‘vulnerable plaque’ (i.e. thin cap fibroateroma) and, phenotypically, constitute a moving target. Thirdly, further accuracy to identify better the distinct histological components of atherosclerotic plaques with IVUS is clearly required. Some technological developments have attempted to overcome the limitations of conventional IVUS to elucidate plaque components.2–6 All of them rely on sophisticated mathematical analyses of the raw radiofrequency signal, aim to obtain real-time clinically meaningful tissue characterization, and provide a “ rainbow ” of appealing colours encoding different plaque components.2–6 Direct use of radiofrequency data avoids signal alterations induced by automated processing and gain adjustments. Integrated backscatter IVUS (IB-IVUS) uses the fast Fourier transformation to analyse frequencies within the signal and provides colour-coded tissue maps of plaque components (lipid, fibrous, and calcified) enabling tissue characterization with a good correlation with histological and angioscopic findings.3 Some provocative preliminary studies have even suggested that IB-IVUS may allow the detection of changes in plaque characteristics induced by lipid-lowering drugs and could also help to predict future coronary events.2,4 Alternative modalities of tissue characterization, namely wavelet analysis of radiofrequency signals, have also been validated to … *Corresponding author. Email: falf{at}hotmail.com

EUS spectrum analysis for in vivo characterization of pancreatic and lymph node tissue: a pilot study

EUS is limited by variability in the examiner's subjective interpretation of B-scan images to differentiate among normal, inflammatory, and malignant tissue. By using information otherwise discarded by conventional EUS systems, quantitative spectral analysis of the raw radiofrequency (RF) signals underlying EUS images enables tissue to be characterized more objectively. Our purpose was to determine the feasibility of using spectral analysis of EUS data for characterization of pancreatic tissue and lymph nodes. A pilot study of eligible patients was conducted to analyze the RF data obtained during EUS by using spectral parameters. Twenty-one subjects who underwent EUS of the esophagus, stomach, pancreas, and surrounding intra-abdominal and mediastinal lymph nodes. Linear regression parameters of calibrated power spectra of the RF signals were tested to differentiate normal pancreas from chronic pancreatitis and from pancreatic cancer as well as benign from malignant-appearing lymph nodes. The mean intercept, slope, and midband fit of the spectra differed significantly among normal pancreas, adenocarcinoma, and chronic pancreatitis when all were compared with each other (P < .01). On direct comparison, mean midband fit for adenocarcinoma differed significantly from that for chronic pancreatitis (P < .05). For lymph nodes, mean midband fit and intercept differed significantly between benign- and malignant-appearing lymph nodes (P < .01 and P < .05, respectively). Small sample population and spatial averaging inherent to this technique. Mean spectral parameters in EUS imaging can provide a noninvasive method to discriminate normal from diseased pancreas and lymph nodes.

WE-B-I-609-01: The Ultrasound Research Interface: A New Tool for Biomedical Investigations

Digitally controlledultrasoundscanners offer extensive levels of programmability, which enable manufacturers to explore and to readily incorporate alternative beam formation, signal and image processing, networking, and interfacing capabilities. Recent efforts have led manufacturers to share these tools for innovation with academic and clinical researchers. This discussion will present capabilities of two such machines and present examples of research uses. The Axius Direct Ultrasound Research Interface (URI) available on the Siemens SONOLINE Antares ™ scanner enables users to acquire raw radio frequency data from regions of interest throughout the image plane. Echo data are available in B‐mode, M‐mode, pulse Doppler and color flow imaging modes. Data can be acquired to a file with a simple button press.Also, user defined scripts can be used to record and reproduce RF acquisitions for combinations of front panel control settings such as transmit level, center frequency, beam angle, and focal depth. The NIH supported development of the interface is complemented by a set of Matlab tools developed at the University of California at Davis, which read echo and image data and perform image processing operations. The Sonix RP research system from Ultrasonix is based on an open PC platform, in which the conventional exam software can run either as a single application or as one of separate windows on the display. The PC also runs connectivity, interface and controlsoftware in parallel. The computer can controlimaging parameters and apply various post‐processing and display methods in real‐time on RF, I/Q and envelope data to output and store the ultrasound information. When in research mode, users can build, develop, and run client software applications in the Microsoft Visual Studio environment to do customized signal processing, carry out special echo acquisition sequences, and synchronize with external devices, such as stepper motors. These tasks can be accomplished either locally or over a network. Examples of the use of these systems that will be outlined include parametric ultrasound imaging, elasticity imaging, and measuring the speed of sound in pulse‐echo mode.

BSS-based filtering of physiological and ARFI-induced tissue and blood motion

Blind source separation (BSS) for adaptive filtering is presented in application to imaging both physiological and acoustic radiation force impulse (ARFI)-induced tissue and blood motion in the common carotid artery. The collected raw radiofrequency (RF) data includes vessel wall motion, blood flow and ARFI-induced motion. In the context of these complex motion patterns, the same BSS adaptive filtering method was employed for three diverse applications: 1. clutter filtering ensembles prior to blood velocity estimation, 2. extracting small axial velocity components from noisy velocity measurements given large flow angles and 3. reducing noise in measured ARFI-induced tissue displacement profiles to enhance differentiation of local tissue structures. The filter separated physiological vessel wall motion from axial blood flow and ARFI-induced motion; successful filter performance is demonstrated in velocity estimates, color flow images and ARFI displacement profiles. The results demonstrate the breadth of applications for BSS adaptive filtering in the clinical imaging environment. (E-mail: caterina.gallippi@duke.edu)

Ultrasonic spectral parameter characterization of apoptosis

Ultrasound (US) spectral analysis methods are used to analyze the radiofrequency (RF) data collected from cell pellets exposed to chemotherapeutics that induce apoptosis and other chemicals that induce nuclear transformations. Calibrated backscatter spectra from regions-of-interest (ROI) were analyzed using linear regression techniques to calculate the spectral slope and midband fit. Two f/2 transducers, with operating frequencies of 30 and 34 MHz (relative bandwidths of 93% and 78%, respectively) were used with a custom-made imaging system that enabled the collection of the raw RF data. For apoptotic cells, the spectral slope increased from 0.37 dB/MHz before drug exposure to 0.57 dB/MHz 24 h after, corresponding to a change in effective scatterer radius from 8.7 to 3.2 μm. The midband fit increased in a time-dependent fashion, peaking at 13dB 24 h after exposure. The statistical deviation of the spectral parameters was in close agreement with theoretical predictions. The results provide a framework for using spectral parameter methods to monitor apoptosis in in vitro and in in vivo systems and are being used to guide the design of system and signal analysis parameters. (E-mail: mkolios@ryerson.ca)

Correlation processing for correction of phase distortions in subaperture imaging

Ultrasonic subaperture imaging combines synthetic aperture and phased array approaches and permits low-cost systems with improved image quality. In subaperture processing, a large array is synthesized using echo signals collected from a number of receive subapertures by multiple firings of a phased transmit subaperture. Tissue inhomogeneities and displacements in subaperture imaging may cause significant phase distortions on received echo signals. Correlation processing on reference echo signals can be used for correction of the phase distortions, for which the accuracy and robustness are critically limited by the signal correlation. In this study, we explore correlation processing techniques for adaptive subaperture imaging with phase correction for motion and tissue inhomogeneities. The proposed techniques use new subaperture data acquisition schemes to produce reference signal sets with improved signal correlation. The experimental test results were obtained using raw radio frequency (RF) data acquired from two different phantoms with 3.5 MHz, 128-element transducer array. The results show that phase distortions can effectively be compensated by the proposed techniques in real-time adaptive subaperture imaging.