Siemens Healthcare Gmbh

Siemens Medical Solutions Usa, Inc.

Siemens Aktiengesellschaft

Siemens Healthineers Ag

The University of Western Ontario

Daphne Yu

Kaloian Petkov

Klaus Engel

Feng Qiu

Wei Hong

Tommaso Mansi

Shun P Miao

Wei Li

Sandra Sudarsky

Christoph Vetter

John Paulus, Jr

Dorin Comaniciu

Bogdan Georgescu

Shaohua Kevin Zhou

Gianluca Paladini

Robert Schneider

Chen Liu

Terrence Chen

Puneet Sharma

Mariappan S Nadar

Vivek Kumar Singh

Yao-Jen Chang

David D Liu

Bernhard Geiger

Stefan Kluckner

Kai Ma

Stefan Assmann

Lining Yang

Jeffrey Paul Johnson

Thomas Friese

Nathan S Lay

John Patrick Collins

Yuri Boykov

Rishabh Shah

John Scott Nafziger

Ruth J Soenius

Thomas Stingl

Andrew Delong

Ludovic Sibille

Marcel Piotraschke

Matthieu Dederichs

Jun Tao

Xiaoke Huang

Lev Gretskii

Babu Swamydoss

Philipp Treffer

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

The present embodiment relates to a renderer and an interactive method for image editing of medical 3D anatomical data. The method includes receiving a dataset with volumetric image data, which have been acquired from an image acquisition modality, and providing a signed distance field data structure of the received dataset. Further, editing operations are received from a user interface for editing at least a part of the provided signed distance field data structure. A visualization of the editing operations is calculated and displayed on a display.

Interactive image editing

Systems and methods are provided rendering a three-dimensional volume. Scan data representing an anatomical object of a patient is acquired. A boundary of the object is identified in the scan data. A first lightmap is positioned inside the boundary of the object. A second lightmap is positioned outside the boundary of the object. The three-dimensional volume of the object is rendered from the scan data with lighting based on the first lightmap and second lightmap.

Internal lighting for endoscopic organ visualization

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

Physically-based volume rendering generates a lightfield. The locations of scattering modeled in physically-based rendering are used to assign depths for the lightfield. The previously assigned depths and previously rendered lightfield are used for lightfield rendering, which may be performed more rapidly than the physically-based volume rendering.

Lightfield rendering based on depths from physically-based volume rendering

Systems and methods are provided for generating a two-dimensional image for identification of medical imaging data. An image processor acquires the medical imaging data and determines a category of the medical imaging data. A machine-learnt network identifies as a function of the category, a plurality of settings of rendering parameters that highlight one or more features the medical imaging data. The image processor renders the two-dimensional identifier image from the medical imaging data using the plurality of settings of rendering parameters and stores the medical imaging data with the two-dimensional identifier image.

Method for memorable image generation for anonymized three-dimensional medical image workflows

An embodiment suggests a method for visualizing an image data set, in particular a medical image data set, wherein the visualized data set displays a three dimensional arrangement having at least a first object and a second object. The method includes assigning a first set of parameter to the first object; assigning a second set of parameters to the second object; dividing the medical image data set into a first sub-region and a second sub-region; and providing a visualisation of the three dimensional arrangement by a volume rendering method, in particular by a ray-casting method or a photorealistic volumetric path tracing, the first set of parameter being applied to the first sub-region for visualizing the first object and the second set of parameter being applied to the second sub-region for visualizing the second object.

In-context photorealistic 3D visualization for surgical decision support

For interactive rendering in medical imaging, physically-based volume rendering of a volume of a patient may better assist physicians in diagnosis, prognosis, and/or planning. To provide for more rapid interaction, direct volume rendering is used during interaction. The rendering then transitions to physically-based rendering when there is no interaction. For smoothing the transition and/or preserving cohesive perceptual details, images from the different types of rendering may be blended in the transition and/or during interaction.

Hybrid interactive mode for rendering medical images with ray tracing

A computer-implemented method of reducing 4D Digital Subtracted Angiography (DSA) reconstruction artifacts using a computational fluid dynamics (CFD) simulation includes a computer receiving first DSA time sequence data comprising a representation of a plurality of vessels and segmenting a vessel of interest from the first DSA time sequence data. The computer uses the CFD simulation to simulate fluid dynamics across the vessel of interest to yield a flow field and determines a plurality of simulated time activity curve parameters for each voxel inside the vessel of interest using the flow field. Then, the computer applies a reconstruction process to second DSA time sequence data to yield a DSA volume. This reconstruction process is constrained by the plurality of simulated time activity curve parameters for each voxel inside the vessel of interest.

CFD simulation assisted 4D DSA reconstruction

A computer-implemented method for performing window-leveling of volumetric ray tracing includes a computer system retrieving a quad-tree data structure organizing a plurality of light paths associated with a volume rendered using a transfer function and one or more window level values. The computer system also receives a user selection of at least one of a new transfer function or new window level values. The volume is rendered by the computer system with the new transfer function or the new window level values using the quad-tree data structure. Then, the computer system synthesizes an image by adding contributions from all of the plurality of light paths according to the quad-tree data structure.

Fast window-leveling of volumetric ray tracing

Physically-based volume rendering produces pixels. By assigning depths to the pixels, a 3D point cloud is generated. For more rapid rendering, such as due to user interaction, the 3D point cloud rendering is a proxy to physically-based volume rendering. The rendering parameters for the desired image may be determined using the proxy, and then physically-based volume rendering is performed to obtain the desired image.

Point cloud proxy for physically-based volume rendering

An artificial intelligence agent is machine trained and used to provide physically-based rendering settings. By using deep learning and/or other machine training, settings of multiple rendering parameters may be provided for consistent imaging even in physically-based rendering.

Content-based medical image rendering based on machine learning

Generating anatomically specific movie driven medical image review is provided. Scan data is received representing an anatomy of a patient. A movie generation preset selection associated with the scan data is received. Anatomical landmarks within the scan data are detected. A movie of the patient is generated based on the scan data, the movie generation preset, and the anatomical landmarks.

Anatomically specific movie driven medical image review

For interactive viewing in medical imaging, pre-rendering is used to provide greater quality of the rendered images with a given computational power. For interactivity in viewing direction, two or more views are rendered for each location and provided as video to the viewer. The viewer selects a viewing direction interactively, and an image at the selected view direction is formed from the previously rendered images of the videos for the two or more views. In an alternative or additional approach, a video graph is created with videos of different segments of the graph. The videos are sequences of images representing change in location, camera parameters (e.g., zooming), and/or rendering settings. Once provided to the user, interactivity is provided by selection of the appropriate node of the video graph. The user may interactively select different locations and/or rendering settings available in the video graph, allowing interaction while benefiting from quality provided by pre-rendered video.

Video-based interactive viewing along a path in medical imaging

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Yardley, PA, United States of America

Daphne Yu