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611 results about "Tubular stenosis" patented technology

A stenosis is an abnormal narrowing in a blood vessel or other tubular organ or structure. It is also sometimes called a stricture (as in urethral stricture).

System and methods for performing endovascular procedures

A system for inducing cardioplegic arrest and performing an endovascular procedure within the heart or blood vessels of a patient. An endoaortic partitioning catheter has an inflatable balloon which occludes the ascending aorta when inflated. Cardioplegic fluid may be infused through a lumen of the endoaortic partitioning catheter to stop the heart while the patient's circulatory system is supported on cardiopulmonary bypass. One or more endovascular devices are introduced through an internal lumen of the endoaortic partitioning catheter to perform a diagnostic or therapeutic endovascular procedure within the heart or blood vessels of the patient. Surgical procedures such as coronary artery bypass surgery or heart valve replacement may be performed in conjunction with the endovascular procedure while the heart is stopped. Embodiments of the system are described for performing: fiberoptic angioscopy of structures within the heart and its blood vessels, valvuloplasty for correction of valvular stenosis in the aortic or mitral valve of the heart, angioplasty for therapeutic dilatation of coronary artery stenoses, coronary stenting for dilatation and stenting of coronary artery stenoses, atherectomy or endarterectomy for removal of atheromatous material from within coronary artery stenoses, intravascular ultrasonic imaging for observation of structures and diagnosis of disease conditions within the heart and its associated blood vessels, fiberoptic laser angioplasty for removal of atheromatous material from within coronary artery stenoses, transmyocardial revascularization using a side-firing fiberoptic laser catheter from within the chambers of the heart, and electrophysiological mapping and ablation for diagnosing and treating electrophysiological conditions of the heart.
Owner:EDWARDS LIFESCIENCES LLC

Feature quantification from multidimensional image data

Techniques, hardware, and software are provided for quantification of extensional features of structures of an imaged subject from image data representing a two-dimensional or three-dimensional image. In one embodiment, stenosis in a blood vessel may be quantified from volumetric image data of the blood vessel. A profile from a selected family of profiles is fit to selected image data. An estimate of cross sectional area of the blood vessel is generated based on the fit profile. Area values may be generated along a longitudinal axis of the vessel, and a one-dimensional profile fit to the generated area values. An objective quantification of stenosis in the vessel may be obtained from the area profile. In some cases, volumetric image data representing the imaged structure may be reformatted to facilitate the quantification, when the structural feature varies along a curvilinear axis. A mask is generated for the structural feature to be quantified based on the volumetric image data. A curve representing the curvilinear axis is determined from the mask by center-finding computations, such as moment calculations, and curve fitting. Image data are generated for oblique cuts at corresponding selected orientations with respect to the curvilinear axis, based on the curve and the volumetric image data. The oblique cuts may be used for suitable further processing, such as image display or quantification.
Owner:GENERAL ELECTRIC CO

Method of and system for intravenous volume tomographic digital angiography imaging

A method of and system for performing intravenous tomographic digital angiography imaging which combines the principles of intravenous digital angiography with those of cone-beam volume tomography for generating a direct, unambiguous and accurate 3-D reconstruction of stenosis and other irregularities and malformations from 2-D cone-beam tomography projections is disclosed in which several different data acquisition geometries, such as a circle-plus-arc data acquisition geometry, may be utilized to provide a complete set of data so that an exact 3-D reconstruction is obtained. Only a single IV contrast injection with a short breathhold by the patient is needed for use with a volume CT scanner which uses a cone-beam x-ray source and a 2-D detector for fast volume scanning in order to provide true 3-D descriptions of vascular anatomy with more than 0.5 lp/mm isotropic resolution in the x, y and z directions is utilized in which one set of cone-beam projections is acquired while rotating the x-ray tube and detector on the CT gantry and then another set of projections is acquired while tilting the gantry by a small angle. The projection data is preweighted and the partial derivatives of the preweighted projection data are calculated. Those calculated partial derivatives are rebinned to the first derivative of the Radon transform, for both the circular orbit data and the arc orbit data. The second partial derivative of the Radon transform is then calculated and then the reconstructed 3-D images are obtained by backprojecting using the inverse Radon transform.
Owner:UNIVERSITY OF ROCHESTER

Imaging based symptomatic classification and cardiovascular stroke risk score estimation

Characterization of carotid atherosclerosis and classification of plaque into symptomatic or asymptomatic along with the risk score estimation are key steps necessary for allowing the vascular surgeons to decide if the patient has to definitely undergo risky treatment procedures that are needed to unblock the stenosis. This application describes a statistical (a) Computer Aided Diagnostic (CAD) technique for symptomatic versus asymptomatic plaque automated classification of carotid ultrasound images and (b) presents a cardiovascular stroke risk score computation. We demonstrate this for longitudinal Ultrasound, CT, MR modalities and extendable to 3D carotid Ultrasound. The on-line system consists of Atherosclerotic Wall Region estimation using AtheroEdge™ for longitudinal Ultrasound or Athero-CTView™ for CT or Athero-MRView from MR. This greyscale Wall Region is then fed to a feature extraction processor which computes: (a) Higher Order Spectra; (b) Discrete Wavelet Transform (DWT); (c) Texture and (d) Wall Variability. The output of the Feature Processor is fed to the Classifier which is trained off-line from the Database of similar Atherosclerotic Wall Region images. The off-line Classifier is trained from the significant features from (a) Higher Order Spectra; (b) Discrete Wavelet Transform (DWT); (c) Texture and (d) Wall Variability, selected using t-test. Symptomatic ground truth information about the training patients is drawn from cross modality imaging such as CT or MR or 3D ultrasound in the form of 0 or 1. Support Vector Machine (SVM) supervised classifier of varying kernel functions is used off-line for training. The Atheromatic™ system is also demonstrated for Radial Basis Probabilistic Neural Network (RBPNN), or Nearest Neighbor (KNN) classifier or Decision Trees (DT) Classifier for symptomatic versus asymptomatic plaque automated classification. The obtained training parameters are then used to evaluate the test set. The system also yields the cardiovascular stroke risk score value on the basis of the four set of wall features.
Owner:SURI JASJIT S
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