Abstract: Disclosed herein is a framework for facilitating waveform parameter estimation. In accordance with one aspect, time-based waveforms are generated based on analysis planes positioned along a centerline of the vessel. A surface may be fitted to upslope regions of the waveforms to determine one or more waveform parameters based on intersection of the surface with the upslope regions.

Abstract: Disclosed herein is a framework for facilitating waveform parameter estimation. In accordance with one aspect, time-based waveforms are generated based on analysis planes positioned along a centerline of the vessel. A surface may be fitted to upslope regions of the waveforms to determine one or more waveform parameters based on intersection of the surface with the upslope regions.

Abstract: A boundary in a medical image is segmented. To increase reproducibility, a multi-level segmentation approach is used. A boundary is detected based on a seed point. The boundary is used to construct a banded graph. Local segmentation is performed using the banded graph. Based on the local segmentation, a new seed point is found. The local segmentation identifies a consistent location for the seed point. The boundary detection is performed again using the new seed point.

Abstract: A method of deriving blood flow parameters from a moving three-dimensional (3D) model of a blood vessel includes determining a reference vascular cross-sectional plane through a location of a lumen in a moving 3D model of the blood vessel at one time within the model, determining a plurality of target vascular cross-sectional planes at multiple times via temporal tracking of the reference plane based on a displacement field, determining a plurality of contours based on an intersection of the target vascular cross-sectional planes with the moving 3D vessel model at multiple times within the model, and determining a blood flow parameter of the vessel from intersections of each contour of a given one of the times with a phase contrast magnetic resonance (PC-MRI) image of the blood vessel from the corresponding time.

Abstract: A boundary in a medical image is segmented. To increase reproducibility, a multi-level segmentation approach is used. A boundary is detected based on a seed point. The boundary is used to construct a banded graph. Local segmentation is performed using the banded graph. Based on the local segmentation, a new seed point is found. The local segmentation identifies a consistent location for the seed point. The boundary detection is performed again using the new seed point.

Abstract: Methods and systems for modeling cerebral aneurysm and their incoming and outgoing vessels from 3D image data are disclosed. Aneurysms and vessels are segmented from their background using a graph-cuts method. Begin and end of vessels are determined. Construction of a centerline of the incoming and outgoing vessels using a measure of vesselness in calculating a minimum cost path in a graph with nodes being representation of pixels is also disclosed. Vessel surface models are constructed from sub-voxel cross-sectional segmentation. The interpolation of vessels inside an aneurysm based on smooth continuity is disclosed. Selection of endo-vascular stents based on interpolation results is also provided.

Abstract: A method for extracting a centerline of a tubular structure in a digital medical image includes providing a 3-dimensional (3D) digitized medical image having a segmented tubular structure, finding a path in the image between a starting point and every other point in the tubular structure that minimizes an accumulative cost function, wherein the minimum accumulative cost ?(x) at a point x is a minimum of (?(x?)+Px,x?) over all nearest neighbors x? wherein Px,x? is a cost of propagation obtained from the inverse of a medialness measure computed in a plane orthogonal to a line between x and x? that is centered at a mid-point of the line, the medialness measure m(x) computed in a circular region C(x, R) centered at point x on the line, with radius R, given by m ? ( x ) = max R ? { 1 N ? ? i = 0 N - 1 ? f ( x , R ? u ? ? ( 2 ? ? ? ? i / N ) ) } , ?wherein {right arrow over (u)}(?)=sin(?){right arrow over (u)}1+cos(?){right arrow over (u)}2 and {right arrow over (u

Abstract: A method of coronary vessel segmentation and visualization includes providing a digitized coronary image, placing a plurality of seed points along an estimated centerline of a coronary vessel, selecting a seed point and constructing a cyclic graph around the seed point in a plane perpendicular to the centerline at the seed point, performing a multi-scale-mean shift filtering in the perpendicular plane to estimate image gradient values, detecting a vessel boundary using a minimum-mean-cycle optimization that minimizes a ratio of a cost of a cycle to a length of a cycle, constructing a sub-voxel accurate vessel boundary about a point on the centerline, and refining the location of the centerline point from the sub-voxel accurate boundary, where the steps of constructing a sub-voxel accurate vessel boundary and refining the centerline point location are repeated until convergence.

Abstract: A method for extracting a local center-axis representation of a vessel, includes: placing first and second seed points in an image that includes the vessel, wherein the first and second seed points are placed near a beginning and an end of a centerline of the vessel; representing the image as a discrete graph having nodes and edges, wherein the first seed point is a source node and the second seed point is a goal node; and finding a minimum-cost path between the first and second seed points by computing a cost of edges between the first and second seed points, wherein the cost of each edge is reciprocal to a vesselness measure of the edge.

Abstract: A method of deriving blood flow parameters from a moving three-dimensional (3D) model of a blood vessel includes determining a reference vascular cross-sectional plane through a location of a lumen in a moving 3D model of the blood vessel at one time within the model, determining a plurality of target vascular cross-sectional planes at multiple times via temporal tracking of the reference plane based on a displacement field, determining a plurality of contours based on an intersection of the target vascular cross-sectional planes with the moving 3D vessel model at multiple times within the model, and determining a blood flow parameter of the vessel from intersections of each contour of a given one of the times with a phase contrast magnetic resonance (PC-MRI) image of the blood vessel from the corresponding time.

Abstract: A method for producing three-dimensional images of a blood vessel. A first set of seed points is placed along a first estimate of a centerline of the vessel. A cyclic graph is constructed around a first one of the seed points in a plane passing through the seed points. The graph comprises a plurality of nodes, with edges connecting the nodes. The nodes are disposed at equally spaced intervals about each one of a circumference of plurality of concentric circles centered at the seed point The method applies filtering such as multi-scale mean shift intensity detection orthogonal to the edges of the cyclic graph to thereby estimate a boundary of the vessel. A new center of the estimated boundary is determined to thereby generate a new seed point. The process is repeated using the new seed point to thereby generate a final boundary of the vessel in the plane.

Abstract: A method for assessing a tumor's response to therapy, includes providing images of a first study of a patient and images of a second study of the patient, the second study occurring after the first study and after the patient undergoes therapy to treat a tumor, each study comprising first and second types of functional magnetic resonance (fMR) images, performing a first registration in which the images within each study are registered, performing a second registration in which reference images from both studies are co-registered, segmenting the tumor in an image of each of the second registered studies; and determining that first and second fMR measure differences exist between the segmented tumor's of the first and second studies, the first fMR measure difference being obtained from the first type of fMR images, the second fMR measure difference being obtained from the second type of fMR images.

Abstract: A method for extracting a centerline of a tubular structure in a digital medical image includes providing a 3-dimensional (3D) digitized medical image having a segmented tubular structure, finding a path in the image between a starting point and every other point in the tubular structure that minimizes an accumulative cost function, wherein the minimum accumulative cost ?(x) at a point x is a minimum of (?(x?)+Px,x?) over all nearest neighbors x? wherein Px,x? is a cost of propagation obtained from the inverse of a medialness measure computed in a plane orthogonal to a line between x and x? that is centered at a mid-point of the line, the medialness measure m(x) computed in a circular region C(x, R) centered at point x on the linr, with radius R, given by m ? ( x ) = max R ? { 1 N ? ? i = 0 N - 1 ? f ( x , R ? u ? ? ( 2 ? ? ? ? i / N ) ) } , wherein {right arrow over (u)}(?)=sin(?){right arrow over (u)}1+cos(?){right arrow over (u)}2 and {right arrow over (u)

Abstract: Methods and systems for modeling cerebral aneurysm and their incoming and outgoing vessels from 3D image data are disclosed. Aneurysms and vessels are segmented from their background using a graph-cuts method. Begin and end of vessels are determined. Construction of a centerline of the incoming and outgoing vessels using a measure of vesselness in calculating a minimum cost path in a graph with nodes being representation of pixels is also disclosed. Vessel surface models are constructed from sub-voxel cross-sectional segmentation. The interpolation of vessels inside an aneurysm based on smooth continuity is disclosed. Selection of endo-vascular stents based on interpolation results is also provided.

Abstract: A method for extracting a local center-axis representation of a vessel, includes: placing first and second seed points in an image that includes the vessel, wherein the first and second seed points are placed near a beginning and an end of a centerline of the vessel; representing the image as a discrete graph having nodes and edges, wherein the first seed point is a source node and the second seed point is a goal node; and finding a minimum-cost path between the first and second seed points by computing a cost of edges between the first and second seed points, wherein the cost of each edge is reciprocal to a vesselness measure of the edge.

Abstract: A method of coronary vessel segmentation and visualization includes providing a digitized coronary image, placing a plurality of seed points along an estimated centerline of a coronary vessel, selecting a seed point and constructing a cyclic graph around the seed point in a plane perpendicular to the centerline at the seed point, performing a multi-scale-mean shift filtering in the perpendicular plane to estimate image gradient values, detecting a vessel boundary using a minimum-mean-cycle optimization that minimizes a ratio of a cost of a cycle to a length of a cycle, constructing a sub-voxel accurate vessel boundary about a point on the centerline, and refining the location of the centerline point from the sub-voxel accurate boundary, where the steps of constructing a sub-voxel accurate vessel boundary and refining the centerline point location are repeated until convergence.

Abstract: A method for producing three-dimensional images of a blood vessel. A first set of seed points is placed along a first estimate of a centerline of the vessel. A cyclic graph is constructed around a first one of the seed points in a plane passing through the seed points. The graph comprises a plurality of nodes, with edges connecting the nodes. The nodes are disposed at equally spaced intervals about each one of a circumference of plurality of concentric circles centered at the seed point The method applies filtering such as multi-scale mean shift intensity detection orthogonal to the edges of the cyclic graph to thereby estimate a boundary of the vessel. A new center of the estimated boundary is determined to thereby generate a new seed point. The process is repeated using the new seed point to thereby generate a final boundary of the vessel in the plane.