39 results about "Coronary arterial tree" patented technology

A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.

Automatic measurement of morphometric and motion parameters of a coronary target includes extracting reference frames from input data of a coronary target at different phases of a cardiac cycle, extracting a three-dimensional centerline model for each phase of the cardiac cycle based on the references frames and projection matrices of the coronary target, tracking a motion of the coronary target through the phases based on the three-dimensional centerline models, and determining a measurement of morphologic and motion parameters of the coronary target based on the motion.

An SYNTAX automatic scoring method based on coronary angiogram image segmentation segments and extracts a coronary arterial tree and central line based on coronary angiogram images of a patient and employs a blood vessel side branch separation, reconnection and K main curve method, and generates a binary image of the coronary arterial tree The blood vessel segment numbers are automatically matched based on the SYNTAX scoring standard by employing a central line direction tracking method, and coronary artery pathological changes and adverse characteristics are identified and scored by selecting a coronary left (right) advantageous type in combination with the width attenuation degree of the cross section of the blood vessel and the curvature change of the center line, and the scores are calculated to give a reasonable score result to allow a doctor to employ optimal operation scheme. The method overcomes the problems of great computation amount, tedious calculating steps, complex scoring standard and long calculating time of artificial calculating SYNTAX scoring in conventional clinics.

A method for non-rigid registration of digital 3D coronary artery models with 2D fluoroscopic images during a cardiac intervention includes providing a digitized 3D centerline representation of a coronary artery tree that comprises a set of S segments composed of Q_S 3D control points, globally aligning the 3D centerline to at least two 2D fluoroscopic images, and non-rigidly registering the 3D centerline to the at least two 2D fluoroscopic images by minimizing an energy functional that includes a summation of square differences between reconstructed centerline points and registered centerline points, a summation of squared 3D registration vectors, a summation of squared derivative 3D registration vectors, and a myocardial branch energy. The non-rigid registration of the 3D centerline is represented as a set of 3D translation vectors r_s,q that are applied to corresponding centerline points x_s,q in a coordinate system of the 3D centerline.

A novel method is presented for detecting coronary arteries as well as other peripheral vessels of the heart. After finding the location of the myocardium through a segmentation method, such as a graph theoretic segmentation method, the method models the heart with a biaxial ellipsoid. For each point of the ellipsoid, a collection of intensities are computed that are normal to the surface. This collection is then filtered to detect the cardiovascular structures. Ultimately, vessel centerline points are detected using a vessel tracking method, and linked together to form a complete coronary artery tree.

A method of generating an image of a coronary artery tree for a patient. The method can include acquiring data from the patient for coronary artery segments and generating a coronary artery tree image including the coronary artery segments. The method can also include accessing coronary artery tree patterns, comparing the coronary artery tree image to the coronary artery tree patterns, and automatically selecting one of the coronary artery tree patterns as a representative coronary artery tree image for the patient. The method can further include measuring lesions and automatically adding the lesion measurements to the coronary artery tree image for the patient.

Compositions, methods of fabrication and methods of treatment for the controlled release of a therapeutic substance to a treatment region are disclosed herein. In some embodiments, block copolymer-based release platforms (modified or unmodified) can be used to deliver a therapeutic substance to an inflamed site in, for example, the coronary tree or the kidney glomeri. The platforms can be carriers of at least one therapeutic substance. An external “trigger”, or stimulus, such as radiation, ultrasound, temperature, a magnetic field, a change in pH, a change in ionic strength or release of an enzyme, can be used to destabilize the platform in order to release its payload in a controlled manner once at a treatment site. Delivery devices can include a syringe, an infusion catheter, a porous balloon catheter, a double balloon catheter and the like.

A method and system for determining hemodynamic indices, such as fractional flow reserve (FFR), for a location of interest in a coronary artery of a patient are disclosed. Medical image data of a patient is received. Patient-specific coronary arterial tree geometry of the patient is extracted from the medical image data. Geometric features are extracted from the patient-specific coronary arterial tree geometry of the patient. A hemodynamic index, such as FFR, is computed for a location of interest in the patient-specific coronary arterial tree based on the extracted geometric features using a trained machine-learning based surrogate model. The machine-learning based surrogate model is trained based on geometric features extracted from synthetically generated coronary arterial tree geometries.

A method and system for non-invasive assessment and therapy planning for coronary artery disease from medical image data of a patient is disclosed. Geometric features representing at least a portion of a coronary artery tree of the patient are extracted from medical image data. Lesions are detected in coronary artery tree of the patient and a hemodynamic quantity of interest is computed at a plurality of points along the coronary artery tree including multiple points within the lesions based on the extracted geometric features using a machine learning model, resulting in an estimated pullback curve for the hemodynamic quantity of interest. Post-treatment values for the hemodynamic quantity of interest are predicted at the plurality of points along the coronary artery tree including the multiple points within the lesions for each of one or more candidate treatment options for the patient, resulting in a respective predicted post-treatment pullback curve for the hemodynamic quantity of interest for each of the one or more candidate treatment options. A visualization of a treatment prediction for at least one of the candidate treatment options is displayed.

Embodiments relate to non-invasively determining coronary circulation parameters during a rest state and a hyperemic state for a patient. The blood flow in the coronary arteries during a hyperemic state provides a functional assessment of the patient's coronary vessel tree. Imaging techniques are used to obtain an anatomical model of the patient's coronary tree. Rest boundary conditions are computed based on non-invasive measurements taken at a rest state, and estimated hyperemic boundary conditions are computed. A feedback control system performs a simulation matching the rest state utilizing a model based on the anatomical model and a plurality of controllers, each controller relating to respective output variables of the coronary tree. The model parameters are adjusted for the output variables to be in agreement with the rest state measurements, and the hyperemic boundary conditions are accordingly adjusted. The hyperemic boundary conditions are used to compute coronary flow and coronary pressure variables.

A medical image processing method of determining a fractional flow reserve through a stenosis of a coronary artery from medical image data is disclosed. The medical image data comprises a set of images of a coronary region of a patient. The coronary region includes the stenosis. The method comprises: reconstructing a three dimensional model of a coronary artery tree of the patient from the medical image data; determining stenosis dimensions from the three dimensional model of the coronary artery tree of the patient; simulating blood flow in the three dimensional model of the coronary artery tree of the patient to determine modeled flow rates; using an analytical model depending on the stenosis dimensions to predict a modeled pressure drop over the stenosis from the modeled flow rates; and determining the fractional flow reserve through the stenosis from the modeled pressure drop.

The present invention is related to a method for reconstruction of the coronary arteries and an examination apparatus for reconstruction of the coronary arteries. To provide improved coronary artery information, an apparatus and a method are provided where a gating signal is provided (32) and a first gated X-ray image sequence of one of the left or right branches of the coronary arteries is acquired (34) with injected contrast agent into the one of the left or right branches of the coronary arteries. Further, a second gated X-ray image sequence of the other branch of the coronary arteries is acquired (36) with injected contrast agent into said other branch. Then, a gated reconstructing (38) of the left and the right coronary artery is suggested and a volume data (40, 42) of the coronary arteries is generated. The volume data of the left and right coronary arteries is registered (44) in relation to time and space. Further; the registered volume data (48, 50) of the left and the right coronary arteries is combined and a combined coronary artery tree volume data set (52) is generated (54). Finally, the combined coronary tree volume data set is visualized (56).

Method and systems are described that create a 3D reconstruction of a vessel of interest that represents a subset of a coronary tree that includes a lesion; calculate at least one of pressure parameters or anatomical parameters based at least in part on a portion of the 3D reconstruction that includes the lesion; calculate a wall shear stress (WSS) descriptor, based on the 3D reconstruction, for a segment of a surface of the vessel that includes the lesion, wherein the WSS descriptor includes information regarding an amount of variation in contraction or expansion applied at surface elements within the segment during at least a portion of a cardiac cycle; and calculate a myocardial infarction (MI) index based on the WSS descriptor and the at least one of the pressure or anatomical parameters, the MI index representing a likelihood that the lesion will result in an MI.

The present invention relates to an apparatus (26) and a method for determining a fractional flow reserve. For this purpose, a new personalized hyperemic boundary condition model is provided. The personalized hyperemic boundary condition model is used to condition a parametric model for a simulation of a blood flow in a coronary tree (34) of a human subject. As a basis for the personalized hyperemic boundary condition 5 model, a predefined hyperemic boundary condition model is used, which represents empirical derived hyperemic boundary condition parameters. However, these empirical hyperemic boundary condition parameters are not specific for a human subject under examination. In order to achieve a specification of the respective predefined hyperemic boundary condition model, specific human subject features are derived from a volumetric image of the coronary 10 tree of the human subject. These features are used to adjust the predefined hyperemic boundary condition model resulting in a personalized hyperemic boundary condition model. As an effect, a flow simulation using the parametric model conditioned by the personalized hyperemic boundary condition model. As an effect, a flow simulation using the parametric model conditioned by the personalized hypefcmtc boundary condition model improves the performance of flow simulation in order to determine an enhanced fractional flow reserve. 15