Siemens Aktiengesellschaft

Beth Israel Deaconess Medical Center, Inc.

Siemens Corporation

Siemens Medical Solutions Usa, Inc.

Hari Sundar

Ali Kamen

Frank Sauer

Rui Liao

John V Frangioni

Chenyang Xu

Vipin Gupta

Amit Kale

Parmeshwar Khurd

Leo John Grady

Ying Zhu

Yanghai Tsin

Thomas Boettger

Wolfgang Wein

Tejas Gajera

Yang Gao

Darko Zikic

Martin Groher

Yunhao Tan

Matthias Schneider

Summer Gibbs-Strauss

Rafiou Oketokoun

A method () for respiratory motion compensation by applying principle component analysis (PCA) on cardiac imaging samples obtained using 2D/3D registration of a pre-operative 3D segmentation of the coronary arteries.

Method of compensation of respiratory motion in cardiac imaging

Methods for analyzing biomedical data include: (a) obtaining macroscopic imaging data; (b) obtaining histopathological imaging data; (c) executing a parallel algorithm stored on a non-transient computer-readable medium to compute one or a plurality of network cycle features of a relative neighborhood graph derived from the histopathological imaging data; (d) registering the macroscopic imaging data and the histopathological imaging data; and (e) correlating the macroscopic imaging data and the network cycle features. Systems for analyzing biomedical data and computer readable storage media are described.

Network cycle features in relative neighborhood graphs

An image mosaicking method includes performing pairwise registration of a plurality of tiles (), determining absolute homographies for each of the plurality of tiles according to the pairwise registration (B), and performing a blending of the plurality of tiles to obtain a stitched image according to the absolute homographies ().

Global error minimization in image mosaicking using graph laplacians and its applications in microscopy

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 Q3D 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 rthat are applied to corresponding centerline points xin a coordinate system of the 3D centerline.

Non-rigid 2D/3D registration of coronary artery models with live fluoroscopy images

A method for segmenting an image includes registering an annotated template image to an acquired reference image using only rigid transformations to define a transformation function relating the annotated template image to the acquired reference image. The defined transformation function is refined by registering the annotated template image to the acquired reference image using only affine transformations. The refined transformation function is further refined by registering the annotated template image to the acquired reference image using only multi-affine transformations. The twice refined transformation function is further refined by registering the annotated template image to the acquired reference image using deformation transformations.

Hierarchical atlas-based segmentation

A method for tracking coronary artery motion includes constructing () a centerline model of a vascular structure in a base phase image in a sequence of 2D images of coronary arteries acquired over a cardiac phase, computing (), for each pixel in a region-of-interest in each subsequent image, a velocity vector that represent a change in position between the subsequent image and base phase image, calculating () positions of control points in each phase using the velocity vectors, and applying () PCA to a P&#xd7;2N data matrix Xconstructed from position vectors (x, y) of N centerline control points for P phases to identify d eigenvectors corresponding to the largest eigenvalues of XXto obtain a d-dimensional linear motion model {circumflex over (&#x3b1;)}, in which a centerline model for a new image at phase p+1 is estimated by adding {circumflex over (&#x3b1;)}to each centerline control point of a previous frame at phase p.

Coronary artery motion modeling

A method for compensating respiratory motion in coronary fluoroscopic images includes finding a set of transformation parameters of a parametric motion model that maximize an objective function that is a weighted normalized cross correlation function of a reference image acquired at a first time that is warped by the parametric motion model and a first incoming image acquired at a second time subsequent to the first time. The weights are calculated as a ratio of a covariance of the gradients of the reference image and the gradients of the first incoming image with respect to a root of a product of a variance of the gradients of the reference image and the variance of the gradients of the first incoming image. The parametric motion model transforms the reference image to match the first incoming image.

System and method for image-based respiratory motion compensation for fluoroscopic coronary roadmapping

A method for performing deformable non-rigid registration of 2D and 3D images of a vascular structure for assistance in surgical intervention includes acquiring 3D image data. An abdominal aorta is segmented from the 3D image data using graph-cut based segmentation to produce a segmentation mask. Centerlines are generated from the segmentation mask using a sequential topological thinning process. 3D graphs are generated from the centerlines. 2D image data is acquired. The 2D image data is segmented to produce a distance map. An energy function is defined based on the 3D graphs and the distance map. The energy function is minimized to perform non-rigid registration between the 3D image data and the 2D image data. The registration may be optimized.

Deformable 2D-3D registration of structure

A medical imaging system to segment an original blood vessel of a body part represented by an original medical image is provided. The system includes an image analyzer for receiving and the original medical image to analyze the original medical image to provide a Hessian Eigen analysis comprising a first data and a second data mapped to each pixel of the medical image, and an image identifier for receiving the Hessian Eigen analysis and for identifying seed points from the pixels by processing the first data and the second data along with a vesselness property, wherein the seed points are used for segmenting the original blood vessel to provide a corrected medical image representing a corrected blood vessel.

Medical imaging system for segementing blood vessel

A method for registering 2-dimensional (2D) images with 3-dimensional (3D) images includes receiving a 2D reference image and a 3D moving image, initializing a registration parameter matrix that rigidly transforms the domain of the moving image, randomly sampling a set of registration parameter matrices in a neighborhood of the initial registration parameters, estimating a cost function for each of the randomly sampled parameter matrices, calculating a distance from each randomly sampled parameter matrix to the initial registration parameter matrix, calculating a mean shift vector from the estimated cost functions and distance, and updating the initial registration parameter matrix from the mean shift vector.

System and method for robust 2D-3D image registration

A medical imaging system for processing an original medical image is provided. The system includes a seed generator for receiving the original medical image representing an original blood vessel and to generate coarse seeds on a basis of vesselness feature in respect to every pixel of the original blood vessel represented by the medical image, and a seed processor for receiving the coarse seeds and for processing the coarse seeds to select a set of refined seeds on a basis of the regression profile for each of the coarse seed by using random forest classification, such that the set of refined seeds are selected from the coarse seeds such that the set of refined seeds adapted to lie on a corrected blood vessel.

Medical imaging system

A system and method for registering a sequence of inter-operative two dimensional (2D) medical images of a target feature with pre-operative three dimensional (3D) medical images of the target feature is disclosed. The 3D image of the target feature is converted to a first skeletal graph. The 2D image of the target feature is converted to a second skeletal graph. Graph matching of the first and second skeletal graphs is performed to obtain a rough alignment of the graphs, and the first and second skeletal graph registered.

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Piscataway, NJ, United States of America

Hari Sundar