Patents by Inventor Sandra Sudarsky

Sandra Sudarsky has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 12354216
    Abstract: 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.
    Type: Grant
    Filed: September 12, 2022
    Date of Patent: July 8, 2025
    Assignee: Siemens Healthineers AG
    Inventors: Kaloian Petkov, Sandra Sudarsky, Rishabh Shah, Christoph Vetter
  • Patent number: 12295776
    Abstract: 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.
    Type: Grant
    Filed: January 24, 2022
    Date of Patent: May 13, 2025
    Assignee: Siemens Healthineers AG
    Inventors: Sandra Sudarsky, Kaloian Petkov
  • Publication number: 20240331269
    Abstract: A hidden structure of a volumetric data set is rendered in a medical image based on a volumetric data set received from a medical scanner. Reference information indicative of a parameter for a clip plane is received for rendering the hidden structure within the medical image. A differentiable rendering, DR, algorithm is applied on the received volumetric data set, which comprises determining a light accumulation along a viewing ray. The clip plane is approximated by including a smooth approximation to a Dirac delta distribution, which depends on the parameter comprised in the received reference information, in the determining of the light accumulation along the viewing ray. The parameter for the clip plane is optimized by optimizing an objective function. The medical image with the hidden structure visible is output for final rendering, and/or for display.
    Type: Application
    Filed: March 14, 2024
    Publication date: October 3, 2024
    Inventor: Sandra Sudarsky
  • Patent number: 12106469
    Abstract: 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.
    Type: Grant
    Filed: March 4, 2022
    Date of Patent: October 1, 2024
    Assignee: Siemens Healthineers AG
    Inventors: Sandra Sudarsky, Kaloian Petkov, Daphne Yu
  • Patent number: 12002147
    Abstract: 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.
    Type: Grant
    Filed: April 20, 2022
    Date of Patent: June 4, 2024
    Assignee: Siemens Healthineers AG
    Inventors: Christoph Vetter, Kaloian Petkov, Rishabh Shah, Sandra Sudarsky
  • Publication number: 20240087218
    Abstract: 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.
    Type: Application
    Filed: September 12, 2022
    Publication date: March 14, 2024
    Inventors: Kaloian Petkov, Sandra Sudarsky, Rishabh Shah, Christoph Vetter
  • Publication number: 20230316638
    Abstract: A convolution neural network, CNN, is trained to determine illumination parameters for image rendering of medical data. For training, a set of images are rendered using a set of rendering configurations, selected from the group of: a set of different camera parameters, a set of different transfer functions for assigning optical properties, like for example color and opacity, to original values of the raw data to be rendered, and a set of different illumination parameters. An evaluation score is computed for representing an amount of image information for each of the rendered images. The computed evaluation score for each rendered image and the rendering configurations applied for rendering the image are used to train the CNN.
    Type: Application
    Filed: March 22, 2023
    Publication date: October 5, 2023
    Inventor: Sandra Sudarsky
  • Publication number: 20230281789
    Abstract: 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.
    Type: Application
    Filed: March 4, 2022
    Publication date: September 7, 2023
    Inventors: Sandra Sudarsky, Kaloian Petkov, Daphne Yu
  • Publication number: 20220343586
    Abstract: 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.
    Type: Application
    Filed: April 20, 2022
    Publication date: October 27, 2022
    Inventors: Christoph Vetter, Kaloian Petkov, Rishabh Shah, Sandra Sudarsky
  • Publication number: 20220287669
    Abstract: 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.
    Type: Application
    Filed: January 24, 2022
    Publication date: September 15, 2022
    Inventors: Sandra Sudarsky, Kaloian Petkov
  • Patent number: 10957098
    Abstract: 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.
    Type: Grant
    Filed: February 13, 2020
    Date of Patent: March 23, 2021
    Assignee: Siemens Healthcare GmbH
    Inventors: Kaloian Petkov, Chen Liu, Shun Miao, Sandra Sudarsky, Daphne Yu, Tommaso Mansi
  • Patent number: 10884490
    Abstract: 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.
    Type: Grant
    Filed: February 27, 2019
    Date of Patent: January 5, 2021
    Assignee: Siemens Healthcare GmbH
    Inventor: Sandra Sudarsky
  • Publication number: 20200272229
    Abstract: 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.
    Type: Application
    Filed: February 27, 2019
    Publication date: August 27, 2020
    Inventor: Sandra Sudarsky
  • Publication number: 20200184708
    Abstract: 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.
    Type: Application
    Filed: February 13, 2020
    Publication date: June 11, 2020
    Inventors: Kaloian Petkov, Chen Liu, Shun Miao, Sandra Sudarsky, Daphne Yu, Tommaso Mansi
  • Patent number: 10607393
    Abstract: 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.
    Type: Grant
    Filed: January 3, 2018
    Date of Patent: March 31, 2020
    Assignee: Siemens Healthcare GmbH
    Inventors: Kaloian Petkov, Chen Liu, Shun Miao, Sandra Sudarsky, Daphne Yu, Tommaso Mansi
  • Patent number: 10593099
    Abstract: 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.
    Type: Grant
    Filed: November 14, 2017
    Date of Patent: March 17, 2020
    Assignee: Siemens Healthcare GmbH
    Inventors: Sandra Sudarsky, Kaloian Petkov
  • Publication number: 20190147639
    Abstract: 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.
    Type: Application
    Filed: November 14, 2017
    Publication date: May 16, 2019
    Inventors: Sandra Sudarsky, Kaloian Petkov
  • Publication number: 20180260997
    Abstract: 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.
    Type: Application
    Filed: January 3, 2018
    Publication date: September 13, 2018
    Inventors: Kaloian Petkov, Chen Liu, Shun Miao, Sandra Sudarsky, Daphne Yu, Tommaso Mansi
  • Patent number: 9830427
    Abstract: A method (100) of aneurysm analysis (110) and virtual stent simulation (120) for endovascular treatment of sidewall intracranial aneurysms.
    Type: Grant
    Filed: June 19, 2012
    Date of Patent: November 28, 2017
    Assignee: Siemens Healthcare GmbH
    Inventors: Sajjad Hussain Baloch, Sandra Sudarsky, Ying Zhu, Ashraf Mohamed, Komal Dutta, Durga Namburu, Puthenveettil Nias, Gary S. Martucci, Thomas Redel
  • Patent number: 9519981
    Abstract: 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 ?.
    Type: Grant
    Filed: July 2, 2012
    Date of Patent: December 13, 2016
    Assignee: Siemens Healthcare GmbH
    Inventors: Sandra Sudarsky, Mariappan S. Nadar, Shanhui Sun, Alban Lefebvre, Bernhard Geiger