Siemens Healthcare Gmbh

Siemens Aktiengesellschaft

Siemens Healthineers Ag

Siemens Medical Solutions Usa, Inc.

Siemens Corporate Research, Inc.

Sandra Sudarsky

Bernhard Geiger

Kaloian Petkov

Daphne Yu

Tommaso Mansi

Shun P Miao

Christophe Chefd'hotel

Christoph Vetter

Rishabh Shah

Chen Liu

Dorin Comaniciu

Mariappan S Nadar

Shanhui Sun

Bernhard Schmidt

Thomas Redel

Justinian Rosca

Michael Scheuering

Thomas Allmendinger

Ying Zhu

Radu Victor Balan

Lutz Gündel

Hasan Ertan Cetingul

Sajjad Hussain Baloch

Alban Lefebvre

Gary S Martucci

Komal Dutta

Indraneel Borgohain

Ashraf Mohamed

Durga Namburu

Puthenveettil Nias

Magdalini-Charikleia Pilatou

Systems and methods for determining rendering parameters based on authored snapshots or templates. In one aspect, clinically relevant snapshots of patient medical data are created by experts to support educational or clinical workflows. Alternatively, the snapshots are created by automation processes from AI-based organ and disease segmentations. In another aspect, clinically relevant templates are generated. Rendering parameters are derived from the snapshots or templates, stored, and then applied for either rendering new data or interactive viewing of existing data.

Systems and methods for automated rendering

An automatic light arrangement for medical visualization includes: providing a medical 3D image (D), providing spatial information about a region of interest (R) in this 3D-image (D) and spatial information about a virtual camera (C), determining a plurality of possible arrangements for light sources (L) by using depth information based on the 3D image (DI) together with the spatial information about the region of interest (R) in this image and the spatial information about the virtual camera (C), wherein valid arrangements are those where shadows (S) on the region of interest (R) are below a predefined threshold, and/or wherein the determination or arrangements is based on a number of predefined perceptual metrics applied specifically to the regions of interest, prioritizing the determined arrangements, and choosing the arrangement with the best prioritization.

System and method for automatic light arrangement for medical visualization

Systems and methods for automatically determining an image quality assessment of a rendered medical image are provided. A rendered medical image is received. One or more measures of interest are extracted from the rendered medical image. An image quality assessment of the rendered medical image is determined using a machine learning based image quality assessment network based on the one or more measures of interest. The image quality assessment of the rendered medical image is output.

Image quality assessment for refinement of imaging rendering parameters for rendering medical images

Distance estimation is optimized in virtual or augmented reality. A distance map of a surgical instrument to a region of interest is determined, at least at the beginning and when a position of the surgical instrument has changed. A render-image is rendered based on a medical 3D image and the position of the surgical instrument, at least at the beginning and when the position of the surgical instrument has changed. At least the region of interest and those parts of the surgical instrument positioned in the volume of the render-image are shown in the render-image. Based on the distance map, at least for a predefined area of the region of interest, visible, acoustic, and/or haptic distance-information is added.

Method and system for optimizing distance estimation

For three-dimensional rendering, a machine-learnt model is trained to generate representation vectors for rendered images formed with different rendering parameter settings. The distances between representation vectors of the images to a reference are used to select the rendered image and corresponding rendering parameters that provides a consistency with the reference. In an additional or different embodiment, optimized pseudo-random sequences are used for physically-based rendering. The random number generator seed is selected to improve the convergence speed of the renderer and to provide higher quality images, such as providing images more rapidly for training compared to using non-optimized seed selection.

Consistent 3D rendering in medical imaging

A method includes presenting an image in a head-mounted display (HMD) controlled by a user. The image is rendered with a transfer function that defines one or more presentation characteristics of voxels in the image. Eye sensors in the HMD are used to determine a region of interest being viewed by the user for longer than a predetermined amount of time. The region of interest comprises a subset of voxels included in the image. The transfer function is automatically adjusted to modify one or more of the presentation characteristics of at least the subset of voxels in response to the region of interest. The presentation of the image in the HMD is modified to present the subset of voxels with modified presentation characteristics.

Transfer function adaptation in virtual reality environments

For rendering in medical imaging, a transfer function is determined. A simple approach to setting the transfer function uses a combination of a rendered image and the voxel data, providing a hybrid of both image and data-driven approaches. A region of interest on a rendered image is used to select some of the voxel data. A characteristic of the selected voxel data is used to determine the transfer function for rendering another image. Both the visual aspect of the rendered image and the voxel data from the scan are used to set the transfer function.

Transfer function determination in medical imaging

A method () of aneurysm analysis () and virtual stent simulation () for endovascular treatment of sidewall intracranial aneurysms.

Method for intracranial aneurysm analysis and endovascular intervention planning

A method for visualizing brain connectivity includes receiving image data including molecular diffusion of brain tissue, constructing a tree data structure from the image data, wherein the tree data structure comprises a plurality of network nodes, wherein each network node is connected to a root of the tree data structure, rendering a ring of a radial layout depicting the tree data structure, wherein a plurality of vertices may be traversed from the top to the bottom, duplicating at least one control point for spline edges sharing a common ancestor, and bundling spline edges by applying a global strength parameter &#x3b2;.

Visualizing brain network connectivity

Methods for computed tomography data-based cycle estimation and four-dimensional reconstruction are provided. A gated reconstruction is derived from CT data acquired without gating using an added artificial trigger. The resulting images for different slices are used to determine local or slice variations over time. The local variations over time for the various slices are combined to create a respiratory cycle signal. This respiratory cycle signal is used to bin the images for different phases, allowing four-dimensional CT reconstruction.

Computed tomography data-based cycle estimation and four-dimensional reconstruction

A method for extracting a colonic centerline includes segmenting a colon from a digital image of a patient's abdomen, selecting one extreme point of the colon as a source point, calculating a first distance transform of every point in the colon that is a distance of a point to the source point, and calculating a second distance transform of every point in the colon, that is a shortest distance of a point to a wall point of the colon. A centerline path is generated through the colon using the first and second distance transforms, starting from a point with a greatest distance to the source point as determined by the first distance transform, and adding points to the centerline path by selecting points with a greatest distance to the source point that are farthest from the wall of the colon using the second distance transform.

Tools for automatic colonic centerline extraction

A system and method for colon unfolding via skeletal subspace deformation comprises: performing a centerline computation on a segmented image for deriving a centerline thereof; computing a distance map utilizing said centerline and said segmented image to derive said distance map; generating a polyhedral model of the lumen of said colon; and utilizing said polyhedral model, said distance map, and said centerline for performing a straightening operation on said centerline.

System and method for colon unfolding via skeletal subspace deformation

A method and apparatus for guiding image reading for colon screening, includes calculating a centerline for a colon; scrolling through a succession of two dimensional (2D) cross-sectional images of a colon in respective image planes, such that a current image plane includes a respective current focus point formed by an intersection of the centerline with the current image plane. The method further includes performing a three-dimensional (3D) region growing process for growing a region of a suitable given thickness around a current seed point in a current image plane, the current seed point being at the respective current focus point in the current image plane, such that the region growing 'floods' folds within a prescribed distance from the current seed point such that part of the region intersects the current image plane. Portions of the region intersecting the current image plane that meets at least one of conditions (A) and (B) are removed from consideration where: condition (A) includes the region intersecting the given current image plane containing a further connected component not containing the current seed point, and containing a further intersection of the image plane with the centerline other than the current seed point, and condition (B) includes the further connected component exhibiting a larger area than that exhibited by a connected component containing the current seed point; and marking remaining portions of the region intersecting the current image plane, not removed from consideration, as seen areas.

Method and system for guided two dimensional colon screening

A method and system for displaying unseen areas in guided two dimensional (2D) colon screening, includes calculating segmentation and colon surface for a colon; calculating a centerline for the colon; designating as a current focus point a current point of intersection of a current portion of the centerline and a current cross-sectional image of a colon in a current image plane; and extracting a colon surface portion in a connected colon component containing the current focus point; and marking voxels of the colon surface portion as a 'seen' area. Unseen areas of the colon surface are calculated by subtracting seen areas from the total colon area of observation. The unseen areas may then be displayed.

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Bedminster, NJ, United States of America

Sandra Sudarsky