Patents by Inventor Sathish Ramani

Sathish Ramani 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: 11353411
    Abstract: Various methods and systems are provided for multi-material decomposition for computed tomography. In one embodiment, a method comprises acquiring, via an imaging system, projection data for a plurality of x-ray spectra, estimating path lengths for a plurality of materials based on the projection data and calibration data for the imaging system, iteratively refining the estimated path lengths based on a linearized model derived from the calibration data, and reconstructing material-density images for each material of the plurality of materials from the iteratively-refined estimated path lengths. By determining path-length estimates in this way without modeling the physics of the imaging system, accurate material decomposition may be performed more quickly and with less sensitivity to changes in physics of the system, and furthermore may be extended to more than two materials.
    Type: Grant
    Filed: June 1, 2020
    Date of Patent: June 7, 2022
    Assignee: GE Precision Healthcare LLC
    Inventors: Sathish Ramani, Mingye Wu, Bruno De Man, Peter Edic
  • Patent number: 11337671
    Abstract: Various methods and systems are provided for spectral computed tomography (CT) imaging. In one embodiment, a method comprises performing a scan of a subject to acquire, with a detector array comprising a plurality of detector elements, projection data of the subject, generating corrected path-length estimates based on the projection data and one or more selected correction functions, and reconstructing at least one material density image based on the corrected path-length estimates. In this way, the fidelity of spectral information is improved, thereby increasing image quality for spectral computed tomography (CT) imaging systems, especially those configured with photon-counting detectors.
    Type: Grant
    Filed: January 13, 2020
    Date of Patent: May 24, 2022
    Assignee: GE Precision Healthcare LLC
    Inventors: Jiahua Fan, Matthew Getzin, Sathish Ramani, Peter Edic
  • Patent number: 11195310
    Abstract: The present disclosure relates to image reconstruction with favorable properties in terms of noise reduction, spatial resolution, detail preservation and computational complexity. The disclosed techniques may include some or all of: a first-pass reconstruction, a simplified datafit term, and/or a deep learning denoiser. In various implementations, the disclosed technique is portable to different CT platforms, such as by incorporating a first-pass reconstruction step.
    Type: Grant
    Filed: August 6, 2018
    Date of Patent: December 7, 2021
    Assignees: GENERAL ELECTRIC COMPANY, RENSSELAER POLYTECHNIC INSTITUTE
    Inventors: Lin Fu, Sathish Ramani, Jie Tang, Bruno Kristiaan Bernard De Man, Jed Douglas Pack, Jiang Hsieh, Ge Wang
  • Publication number: 20210372951
    Abstract: Various methods and systems are provided for multi-material decomposition for computed tomography. In one embodiment, a method comprises acquiring, via an imaging system, projection data for a plurality of x-ray spectra, estimating path lengths for a plurality of materials based on the projection data and calibration data for the imaging system, iteratively refining the estimated path lengths based on a linearized model derived from the calibration data, and reconstructing material-density images for each material of the plurality of materials from the iteratively-refined estimated path lengths. By determining path-length estimates in this way without modeling the physics of the imaging system, accurate material decomposition may be performed more quickly and with less sensitivity to changes in physics of the system, and furthermore may be extended to more than two materials.
    Type: Application
    Filed: June 1, 2020
    Publication date: December 2, 2021
    Inventors: Sathish Ramani, Mingye Wu, Bruno De Man, Peter Edic
  • Patent number: 11176642
    Abstract: A computer-implemented method for image processing is provided. The method includes acquiring multiple multi-energy spectral scan datasets and computing basis material images representative of multiple basis materials from the multi-energy spectral scan datasets, wherein the multiple basis material images include correlated noise. The method also includes jointly denoising the multiple basis material images in at least a spectral domain utilizing a deep learning-based denoising network to generate multiple de-noised basis material images.
    Type: Grant
    Filed: July 9, 2019
    Date of Patent: November 16, 2021
    Assignee: GE PRECISION HEALTHCARE LLC
    Inventors: Sathish Ramani, Lin Fu, Bruno Kristiaan Bernard De Man
  • Publication number: 20210212656
    Abstract: Various methods and systems are provided for spectral computed tomography (CT) imaging. In one embodiment, a method comprises performing a scan of a subject to acquire, with a detector array comprising a plurality of detector elements, projection data of the subject, generating corrected path-length estimates based on the projection data and one or more selected correction functions, and reconstructing at least one material density image based on the corrected path-length estimates. In this way, the fidelity of spectral information is improved, thereby increasing image quality for spectral computed tomography (CT) imaging systems, especially those configured with photon-counting detectors.
    Type: Application
    Filed: January 13, 2020
    Publication date: July 15, 2021
    Inventors: Jiahua Fan, Matthew Getzin, Sathish Ramani, Peter Edic
  • Publication number: 20210012463
    Abstract: A computer-implemented method for image processing is provided. The method includes acquiring multiple multi-energy spectral scan datasets and computing basis material images representative of multiple basis materials from the multi-energy spectral scan datasets, wherein the multiple basis material images include correlated noise. The method also includes jointly denoising the multiple basis material images in at least a spectral domain utilizing a deep learning-based denoising network to generate multiple de-noised basis material images.
    Type: Application
    Filed: July 9, 2019
    Publication date: January 14, 2021
    Inventors: Sathish Ramani, Lin Fu, Bruno Kristiaan Bernard De Man
  • Publication number: 20200043204
    Abstract: The present disclosure relates to image reconstruction with favorable properties in terms of noise reduction, spatial resolution, detail preservation and computational complexity. The disclosed techniques may include some or all of: a first-pass reconstruction, a simplified datafit term, and/or a deep learning denoiser. In various implementations, the disclosed technique is portable to different CT platforms, such as by incorporating a first-pass reconstruction step.
    Type: Application
    Filed: August 6, 2018
    Publication date: February 6, 2020
    Inventors: Lin Fu, Sathish Ramani, Jie Tang, Bruno Kristiaan Bernard De Man, Jed Douglas Pack, Jiang Hsieh, Ge Wang
  • Patent number: 10543361
    Abstract: A system and method for localizing a deep brain stimulation electrode in vivo in a subject or object is provided. A magnetic resonance imaging system obtains MR image data from a volume-of-interest by way of a zero echo time (ZTE) or ultrashort echo time (UTE) pulse sequence, with one or more of a phase domain image and a magnitude domain image being analyzed from the MR image data acquired by the ZTE or UTE pulse sequence. One or more electrodes are localized within the volume-of-interest based on an analysis of the phase domain image and/or magnitude domain image. In localizing the electrodes, a multi-scale correlation-based analysis of the volume-of-interest is performed to estimate at least one of an electrode center and electrode contact locations of the electrode, with the localization being achieved with a fast scan-time and with a high level of accuracy and precision.
    Type: Grant
    Filed: February 20, 2018
    Date of Patent: January 28, 2020
    Assignee: General Electric Company
    Inventors: Sathish Ramani, Rolf Schulte, Ileana Hancu, Jeffrey Ashe, Graeme C. McKinnon
  • Patent number: 10475214
    Abstract: The present approach relates to the use of machine learning and deep learning systems suitable for solving large-scale, space-variant tomographic reconstruction and/or correction problems. In certain embodiments, a tomographic transform of measured data obtained from a tomography scanner is used as an input to a neural network. In accordance with certain aspects of the present approach, the tomographic transform operation(s) is performed separate from or outside the neural network such that the result of the tomographic transform operation is instead provided as an input to the neural network. In addition, in certain embodiments, one or more layers of the neural network may be provided as wavelet filter banks.
    Type: Grant
    Filed: April 5, 2017
    Date of Patent: November 12, 2019
    Assignee: GENERAL ELECTRIC COMPANY
    Inventors: Lin Fu, Sathish Ramani, Bruno Kristiaan Bernard De Man, Xue Rui, Sangtae Ahn
  • Patent number: 10448915
    Abstract: A method for characterizing anatomical features includes receiving scanned data and image data corresponding to a subject. The scanned data comprises sinogram data. The method further includes identifying a first region in an image of the image data corresponding to a region of interest. The method also includes determining a second region in the scanned data. The second region corresponds to the first region. The method further includes identifying a sinogram trace corresponding to the region of interest. The sinogram trace comprises sinogram data present within the second region. The method includes determining a data feature of the subject based on the sinogram trace and a deep learning network. The method also includes determining a diagnostic condition corresponding to a medical condition of the subject based on the data feature.
    Type: Grant
    Filed: June 27, 2017
    Date of Patent: October 22, 2019
    Assignee: General Electric Company
    Inventors: Bruno Kristiaan Bernard De Man, Jed Douglas Pack, Eri Haneda, Sathish Ramani, Jiang Hsieh, James Vradenburg Miller, Peter Michael Edic
  • Publication number: 20190255315
    Abstract: A system and method for localizing a deep brain stimulation electrode in vivo in a subject or object is provided. A magnetic resonance imaging system obtains MR image data from a volume-of-interest by way of a zero echo time (ZTE) or ultrashort echo time (UTE) pulse sequence, with one or more of a phase domain image and a magnitude domain image being analyzed from the MR image data acquired by the ZTE or UTE pulse sequence. One or more electrodes are localized within the volume-of-interest based on an analysis of the phase domain image and/or magnitude domain image. In localizing the electrodes, a multi-scale correlation-based analysis of the volume-of-interest is performed to estimate at least one of an electrode center and electrode contact locations of the electrode, with the localization being achieved with a fast scan-time and with a high level of accuracy and precision.
    Type: Application
    Filed: February 20, 2018
    Publication date: August 22, 2019
    Inventors: Sathish Ramani, Rolf Schulte, Ileana Hancu, Jeffrey Ashe, Graeme C. McKinnon
  • Publication number: 20180368781
    Abstract: A method for characterizing anatomical features includes receiving scanned data and image data corresponding to a subject. The scanned data comprises sinogram data. The method further includes identifying a first region in an image of the image data corresponding to a region of interest. The method also includes determining a second region in the scanned data. The second region corresponds to the first region. The method further includes identifying a sinogram trace corresponding to the region of interest. The sinogram trace comprises sinogram data present within the second region. The method includes determining a data feature of the subject based on the sinogram trace and a deep learning network. The method also includes determining a diagnostic condition corresponding to a medical condition of the subject based on the data feature.
    Type: Application
    Filed: June 27, 2017
    Publication date: December 27, 2018
    Inventors: Bruno Kristiaan Bernard DE MAN, Jed Douglas PACK, Eri HANEDA, Sathish RAMANI, Jiang HSIEH, James Vradenburg MILLER, Peter Michael EDIC
  • Publication number: 20180293762
    Abstract: The present approach relates to the use of machine learning and deep learning systems suitable for solving large-scale, space-variant tomographic reconstruction and/or correction problems. In certain embodiments, a tomographic transform of measured data obtained from a tomography scanner is used as an input to a neural network. In accordance with certain aspects of the present approach, the tomographic transform operation(s) is performed separate from or outside the neural network such that the result of the tomographic transform operation is instead provided as an input to the neural network. In addition, in certain embodiments, one or more layers of the neural network may be provided as wavelet filter banks.
    Type: Application
    Filed: April 5, 2017
    Publication date: October 11, 2018
    Inventors: Lin Fu, Sathish Ramani, Bruno Kristiaan Bernard De Man, Xue Rui, Sangtae Ahn
  • Patent number: 9931095
    Abstract: According to some embodiments, system and methods are provided comprising determining one or more image locations at which motion occurred within an image volume of an object containing movable anatomical features prior to segmenting the movable anatomical features; estimating motion at each of the one or more image locations; correcting one or more motion artifacts in the image volume at each of the one or more image locations, where motion was estimated resulting in the generation of a final motion compensated image; and segmenting one or more features of interest in the final motion compensated image. Numerous other aspects are provided.
    Type: Grant
    Filed: March 30, 2016
    Date of Patent: April 3, 2018
    Assignee: GENERAL ELECTRIC COMPANY
    Inventors: Jed Douglas Pack, Brian Edward Nett, Sathish Ramani
  • Publication number: 20170281112
    Abstract: According to some embodiments, system and methods are provided comprising determining one or more image locations at which motion occurred within an image volume of an object containing movable anatomical features prior to segmenting the movable anatomical features; estimating motion at each of the one or more image locations; correcting one or more motion artifacts in the image volume at each of the one or more image locations, where motion was estimated resulting in the generation of a final motion compensated image; and segmenting one or more features of interest in the final motion compensated image. Numerous other aspects are provided.
    Type: Application
    Filed: March 30, 2016
    Publication date: October 5, 2017
    Inventors: Jed Douglas PACK, Brian Edward NETT, Sathish RAMANI
  • Publication number: 20170221235
    Abstract: The present approach relates to the use of a database (i.e., a dictionary) of image patterns to be avoided or de-emphasized during an image reconstruction process, such as an iterative image reconstruction process. Such a dictionary may be characterized as a negative or “bad” dictionary. The negative dictionary may be used to constrain an image reconstruction process to avoid or minimize the presence of the patterns present in the negative dictionary.
    Type: Application
    Filed: February 1, 2016
    Publication date: August 3, 2017
    Inventors: Bruno Kristiaan Bernard De Man, Lin Fu, Jiajia Luo, Eri Haneda, Sathish Ramani
  • Patent number: 9489752
    Abstract: Methods, systems, and non-transitory computer readable media for image reconstruction are presented. Measured data corresponding to a subject is received. A preliminary image update in a particular iteration is determined based on one or more image variables computed using at least a subset of the measured data in the particular iteration. Additionally, at least one momentum term is determined based on the one or more image variables computed in the particular iteration and/or one or more further image variables computed in one or more iterations preceding the particular iteration. Further, a subsequent image update is determined using the preliminary image update and the momentum term. The preliminary image update and/or the subsequent image update are iteratively computed for a plurality of iterations until one or more termination criteria are satisfied.
    Type: Grant
    Filed: October 4, 2013
    Date of Patent: November 8, 2016
    Assignee: General Electric Company
    Inventors: Donghwan Kim, Sathish Ramani, Jeffrey A. Fessler, Lin Fu, Bruno Kristiaan Bernard De Man
  • Patent number: 9406154
    Abstract: The use of the channelized preconditioners in iterative reconstruction is disclosed. In certain embodiments, different channels correspond to different frequency sub-bands and the output of the different channels can be combined to update an image estimate used in the iterative reconstruction process. While individual channels may be relatively simple, the combined channels can represent complex spatial variant operations. The use of channelized preconditioners allows empirical adjustment of individual channels.
    Type: Grant
    Filed: June 14, 2013
    Date of Patent: August 2, 2016
    Assignee: General Electric Company
    Inventors: Lin Fu, Madison Gray McGaffin, Zhou Yu, Jean-Baptiste Thibault, Sathish Ramani, Jeffrey Allen Fessler, Bruno Kristiaan Bernard De Man, Debashish Pal
  • Publication number: 20140369581
    Abstract: The use of the channelized preconditioners in iterative reconstruction is disclosed. In certain embodiments, different channels correspond to different frequency sub-bands and the output of the different channels can be combined to update an image estimate used in the iterative reconstruction process. While individual channels may be relatively simple, the combined channels can represent complex spatial variant operations. The use of channelized preconditioners allows empirical adjustment of individual channels.
    Type: Application
    Filed: June 14, 2013
    Publication date: December 18, 2014
    Inventors: Lin Fu, Madison Gray McGaffin, Zhou Yu, Jean-Baptiste Thibault, Sathish Ramani, Jeffrey Allen Fessler, Bruno Kristiaan Bernard De Man, Debashish Pal