Vital Images, Inc.

Vida Diagnostics, Inc.

Samuel W Peterson

Marek Brejl

Juerg Tschirren

John D Newell, Jr

Pascal Salazar-Ferrer

Benj Thomas

Annapoorani Gothandaraman

Yan Yang

Marguerite Kirkby

Zhujiang Cao

Xia Huang

Thomas J Gleeson

A method for analyzing a patient based on a volumetric pulmonary scan includes receiving volumetric pulmonary scan data representative of a patient's pulmonary structure. This method also includes determining a level of transpulmonary pressure defining an effort metric based on one or more characteristics from the received volumetric pulmonary scan data. This method further includes determining one or more physiological or anatomical parameters associated with the transpulmonary pressure based on the received volumetric pulmonary scan data and the effort metric. A non-transitory computer readable medium can be programmed with instructions for causing one or more processors to perform the method for analyzing a patient based on a volumetric pulmonary scan.

Pulmonary analysis using transpulmonary pressure

A method for analyzing an anatomical structure of a patient may include the steps of receiving volumetric scan data representative of one or more features of an anatomical structure; mapping the features to a node tree diagram; and displaying the node tree diagram. The features can comprise branching points, pathways connecting the branching points, and location data of the branching points and pathways. The node tree diagram may comprise a plurality of nodes and branches representing the branching points and pathways in the anatomical structure, respectively. The plurality of nodes may comprise a root node representing a root branching point as well as additional nodes representing additional branching points. Additionally, the node tree diagram may comprise a first set of one or more regions, wherein each region encompasses a respective portion of the node tree diagram and is representative of a defined portion of the anatomical structure.

Subject specific coordinatization and virtual navigation systems and methods

A method for visualizing a tubular object from a set of volumetric data may include the steps of: determining a viewing direction for the tubular object; selecting a constraint subset of the tubular object within the volumetric data; defining a cut-surface through the volumetric data and including the constraint subset of the tubular object within the volumetric data; and rendering an image based upon the determined viewing direction and the volumetric data of the tubular object along the intersection of the volumetric data and the defined cut-surface. Additionally or alternatively, the method may identify a plurality of bifurcations in the tubular object; assign a weighting factor to each identified bifurcation; determine a bifurcation normal vector associated with each bifurcation; determine a weighted average of the bifurcation normal vectors; and render an image of the volumetric data from a perspective parallel to the weighted average of the bifurcation normal vectors.

Cut-surface display of tubular structures

Techniques for generating a hollow model from a medical image are disclosed herein. In an example, a hollow model may be created within a medical imaging visualization application through the generation of a mask of an interior space of a segmented anatomical structure, the extrusion of a shell mask from the mask of the interior space, and the generation of a visualization of the shell mask within the medical imaging visualization application. For example, the mask may be provided as a layer in the medical imaging visualization application, allowing a user to visualize the produced shell mask of the hollow model from the perspective of the medical imaging. In further examples, the hollow model generation techniques may be used with techniques for shell region modifications, variable shell thickness, multiple shell layer, and trimming of shell endpoints.

Hollow object model visualization in medical images

A method including searching image data corresponding to a series of axial image slices with a processor, searching axial image slices from a starting image slice and calculating a confidence score that an image slice includes a cross-section image of an aorta, identifying an image slice containing at least one seed disk, including an ascending aorta seed disk, from candidate image slices identified according to the confidence score, and growing a 3D segmentation of the ascending aorta by stacking ascending aorta image disks included in consecutive image slices beginning from the ascending aorta seed disk.

Automatic aortic detection and segmentation in three-dimensional image data

A method of operating on volumetric imaging data representing organ anatomy includes determining whether a polyp location within a specified range from a viewing point is visible, or hidden by an anatomical feature, and marking the polyp as visible or hidden with a viewable indicator.

Systems and methods for virtual identification of polyps

A user interface for display or editing of a three dimensional medical image is provided on a display and is responsive to user inputs in a medical imaging system. The user interface includes a framing tool for defining a region of interest on the medical image. An image matrix is then provided on the user interface, which includes a plurality of cross-sectional images each corresponding to a cross-section of the medical image at one of a plurality of cut planes within the region of interest. One or more reference views of the medical image are also displayed, which each include a plurality of reference indicia, each of which corresponds to a location of one of the plurality of cut planes.

User interface for iterative image modification

This document discusses, among other things, systems and methods for efficiently calculating a colon segmentation from one or more candidate virtual three-dimensional objects. A sequence of image scans are analyzed and candidate segments are identified. Landmark segments are identified from the candidate segments. A characteristic path is generated for each candidate segment. The paths are joined using a cost network and reoriented to be consistent with a typical flythrough path. The connected path is then used to generate a continuous volumetric virtual object.

Characteristic path-based colon segmentation

This document discusses, among other things, systems and methods for efficiently calculating a registration of multiple characteristic paths of a virtual three-dimensional object. Each path of discrete points is transformed into a piecewise linear parameterization as a function of path length. The paths are smoothed and normalized. The shorter path is partitioned into a number of discrete subintervals. The subintervals are mapped to the longer path using a minimization function that minimizes a cost function resulting in a locally optimal registration. The shorter path is incrementally positioned along the longer path and the minimization is attempted at each position. When the shorter path cannot be shifted any farther, the globally optimal registration is returned.

Colon characteristic path registration

This document discusses, among other things, systems and methods for efficiently calculating a colon segmentation from one or more candidate virtual three-dimensional objects. A sequence of image scans are analyzed and regions that represent air-filled objects and tagged-stool are identified as candidate segments. A characteristic path is generated for each candidate segment. The paths are joined using a cost network and re-oriented to be consistent with a typical flythrough path. The connected path is then used to generate a continuous volumetric virtual object.

Growing community of inventors

Saint Paul, MN, United States of America

Samuel W Peterson