Intravascular ultrasound (IVUS) has been proven a reliable imaging modality that is widely employed in cardiac
interventional procedures. It can provide morphologic as well as pathologic information on the occluded plaques in the
coronary arteries. In this paper, we present a new technique using wavelet packet analysis that differentiates between
blood and non-blood regions on the IVUS images. We utilized the multi-channel texture segmentation algorithm based
on the discrete wavelet packet frames (DWPF). A k-mean clustering algorithm was deployed to partition the extracted
textural features into blood and non-blood in an unsupervised fashion. Finally, the geometric and statistical information
of the segmented regions was used to estimate the closest set of pixels to the lumen border and a spline curve was fitted
to the set. The presented algorithm may be helpful in delineating the lumen border automatically and more reliably prior
to the process of plaque characterization, especially with 40 MHz transducers, where appearance of the red blood cells
renders the border detection more challenging, even manually. Experimental results are shown and they are
quantitatively compared with manually traced borders by an expert. It is concluded that our two dimensional (2-D)
algorithm, which is independent of the cardiac and catheter motions performs well in both in-vivo and in-vitro cases.
KEYWORDS: Elastography, Finite element methods, Ultrasonography, Ischemia, Echocardiography, Human subjects, Image analysis, In vivo imaging, 3D modeling, Heart
Several methods have been introduced in the past few years to quantify left-ventricular strain in order to detect myocardial ischemia and infarction. Myocardial Elastography is one of these methods, which is based on ultrasound Radio-Frequency (RF) signal processing at high frame rates for the highest precision and resolution of strain estimation. Myocardial elastography estimates displacement and strain during the natural contraction of the myocardium using cross-correlation techniques. We have previously shown that imaging of the myocardial strain at high precision allows the correct assessment of the contractility of the cardiac muscle and thus measurement of the extent of ischemia or infarct. In this paper, for the first time in echocardiography, we show how angle-independent techniques can be used to estimate and image the mechanics of normal and pathological myocardia, both in simulations and in vivo. First, the fundamental limits of 2D normal and principal strain component estimation are determined using an ultrasound image formation model and a 2D short-axis view of a 3D left-ventricular, finite-element model, in normal and ischemic configurations. Two-dimensional (i.e., lateral and axial) cumulative displacement and strain components were iteratively estimated and imaged using 1D cross-correlation and recorrelation techniques in a 2D search. Validation of these elastographic findings in one normal human subject was performed. Principal strains were also imaged for the characterization of normal myocardium. In conclusion, the feasibility of angle-independent, 2D myocardial elastography technique was shown through the calculation of the in-plane principal strains, which was proven essential in the reliable depiction of strains independent of the beam-tissue angle or the type of sonographic view used.
Left-ventricular (LV) segmentation is essential in the early detection of heart disease, where left-ventricular wall motion is being tracked in order to detect ischemia. In this paper, a new method for automated segmentation of the left-ventricular chamber is described. An autocorrelation-based technique isolates the LV cavity from the myocardial wall on 2-D slices of 3D short-axis echocardiograms. A morphological closing function and median filtering are used to generate a uniform border. The proposed segmentation technique is designed to be used in identifying the endocardial border and estimating the motion of the endocardial wall over a cardiac cycle. To this purpose, the proposed technique is particularly successful in border delineation by tracing around structures like papillary muscles and the mitral valve, which constitute the typical obstacle in LV segmentation techniques. The results using this new technique are compared to the manual detection results in short-axis views obtained at the papillary muscle level from 3D datasets in human and canine experiments in vivo. Qualitatively, the automatically-detected borders are highly comparable to the manually-detected borders enclosing regions in the left-ventricular cavity with a relative error within the range of 4.2% - 6%. The new technique constitutes, thus, a robust segmentation method for automated segmentation of endocardial borders and suitable for wall motion tracking for automated detection of ischemia.
Several techniques have been developed in an effort to estimate mechanical properties of tissues. In this paper, we will discuss the advantages of utilizing a new technique that performs RF signal tracking in order to estimate the localized oscillatory motion resulting from a harmonic radiation force produced by two focused ultrasound transducer elements with overlapping beams oscillating at distinct frequencies. Finite-element and Monte-Carlo simulations were performed in order to characterize the range of oscillatory displacements produced by a harmonic radiation force. The frequencies investigated ranged from 200 Hz to 800 Hz and the stiffness between 20 and 80 kPa. In the experimental verification, three transducers were utilized: two focused transducers at 3.75 MHz and a diagnostic transducer 1.1 MHz operating at pulse/receive mode. Agar gels of 7 - 95 kPa stiffness were utilized. Displacement estimates were obtained during the application of the radiation force oscillating at frequencies of 200 - 800 Hz. In experiments, the estimated oscillatory displacement spanned from -800 to 600 microns depending on the magnitude of the applied radiation force. A frequency upshift and an exponential displacement decrease were obtained with stiffness increase in experiments and simulations. These preliminary results demonstrate the feasibility of imaging localized harmonic motion as induced by an oscillatory ultrasound radiation force.
Estimation of the mechanical properties of the cardiac muscle has been shown to play a crucial role in the detection of cardiovascular disease. Elastography was recently shown feasible on RF cardiac data in vivo. In this paper, the role of elastography in the detection of ischemia/infarct is explored with simulations and in vivo experiments. In finite-element simulations of a portion of the cardiac muscle containing an infarcted region, the cardiac cycle was simulated with successive compressive and tensile strains ranging between -30% and 20%. The incremental elastic modulus was also mapped uisng adaptive methods. We then demonstrated this technique utilizing envelope-detected sonographic data (Hewlett-Packard Sonos 5500) in a patient with a known myocardial infarction. In cine-loop and M-Mode elastograms from both normal and infarcted regions in simulations and experiments, the infarcted region was identifed by the up to one order of magnitude lower incremental axial displacements and strains, and higher modulus. Information on motion, deformation and mechanical property should constitute a unique tool for noninvasive cardiac diagnosis.
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