Patents by Inventor Jhimli Mitra
Jhimli Mitra 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).
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Patent number: 11980492Abstract: A computer-implemented method includes generating, via a processor, synthetic vessels. The method also includes performing, via the processor, three-dimensional (3D) computational fluid dynamics (CFD) on the synthetic vessels for different flow rates to generate 3D CFD data. The method further includes extracting, via the processor, 3D image patches from the synthetic vessels. The method even further includes obtaining, via the processor, pressure drops across the 3D image patches from the 3D CFD data. The method yet further includes training, via the processor, a deep neural network utilizing the 3D image patches, the pressure drops, and associated flow rates to generate a trained deep neural network.Type: GrantFiled: November 5, 2021Date of Patent: May 14, 2024Assignee: GE PRECISION HEALTHCARE LLCInventors: Prem Venugopal, Cynthia Elizabeth Landberg Davis, Jed Douglas Pack, Jhimli Mitra, Soumya Ghose, Peter Michael Edic
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Patent number: 11948677Abstract: Systems and techniques that facilitate hybrid unsupervised and supervised image segmentation are provided. In various embodiments, a system can access a computed tomography (CT) image depicting an anatomical structure. In various aspects, the system can generate, via an unsupervised modeling technique, at least one class probability mask of the anatomical structure based on the CT image. In various instances, the system can generate, via a deep-learning model, an image segmentation based on the CT image and based on the at least one class probability mask.Type: GrantFiled: June 8, 2021Date of Patent: April 2, 2024Assignee: GE PRECISION HEALTHCARE LLCInventors: Soumya Ghose, Jhimli Mitra, Peter M Edic, Prem Venugopal, Jed Douglas Pack
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Publication number: 20230309836Abstract: A method for determining a recurrence of a disease in a patient is presented. The method includes generating a plurality of medical images of an organ of the patient and determining a plurality of recurrence probabilities from the plurality of medical images. A recurrence of the disease is determined based on the plurality of recurrence probabilities and clinicopathological data of the patient using a Bayesian network.Type: ApplicationFiled: November 7, 2022Publication date: October 5, 2023Applicant: The Trustees of Indiana UniversityInventors: Souyma Ghose, Zhanpan Zhang, Sanghee Cho, Fiona Ginty, Cynthia Elizabeth Landberg Davis, Jhimli Mitra, Sunil S. Badve, Yesim Gokmen-Polar, Elizabeth Mary McDonough
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Publication number: 20230317293Abstract: A method for determining a recurrence of a disease in a patient is presented. The method includes generating a plurality of medical images of an organ of the patient and determining a plurality of recurrence probabilities from the plurality of medical images. A recurrence of the disease is determined based on the plurality of recurrence probabilities and clinicopathological data of the patient using a Bayesian network.Type: ApplicationFiled: March 31, 2022Publication date: October 5, 2023Inventors: Sanghee Cho, Zhanpan Zhang, Soumya Ghose, Fiona Ginty, Cynthia Elizabeth Landberg Davis, Jhimli Mitra, Sunil S. Badve, Yesim Gokmen-Polar
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Publication number: 20230142152Abstract: A computer-implemented method includes generating, via a processor, synthetic vessels. The method also includes performing, via the processor, three-dimensional (3D) computational fluid dynamics (CFD) on the synthetic vessels for different flow rates to generate 3D CFD data. The method further includes extracting, via the processor, 3D image patches from the synthetic vessels. The method even further includes obtaining, via the processor, pressure drops across the 3D image patches from the 3D CFD data. The method yet further includes training, via the processor, a deep neural network utilizing the 3D image patches, the pressure drops, and associated flow rates to generate a trained deep neural network.Type: ApplicationFiled: November 5, 2021Publication date: May 11, 2023Inventors: Prem Venugopal, Cynthia Elizabeth Landberg Davis, Jed Douglas Pack, Jhimli Mitra, Soumya Ghose, Peter Michael Edic
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Publication number: 20230144624Abstract: A computer-implemented method includes obtaining, via a processor, segmented image patches of a vessel along a coronary tree path and associated coronary flow distribution for respective vessel segments in the segmented image patches. The method also includes determining, via the processor, a pressure drop distribution along an axial length of the vessel from the segmented image patches and the associated coronary flow distribution. The method further includes determining, via the processor, critical points in the pressure drop distribution. The method even further includes detecting, via the processor, a presence of a stenosis based on the critical points in the pressure drop distribution.Type: ApplicationFiled: November 5, 2021Publication date: May 11, 2023Inventors: Prem Venugopal, Cynthia Elizabeth Landberg Davis, Jed Douglas Pack, Jhimli Mitra, Soumya Ghose
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Patent number: 11583188Abstract: In accordance with the present disclosure, deep-learning techniques are employed to find anomalies corresponding to bleed events. By way of example, a deep convolutional neural network or combination of such networks may be trained to determine the location of a bleed event, such as an internal bleed event, based on ultrasound data acquired at one or more locations on a patient anatomy. Such a technique may be useful in non-clinical settings.Type: GrantFiled: March 18, 2019Date of Patent: February 21, 2023Assignee: General Electric CompanyInventors: Jhimli Mitra, Luca Marinelli, Asha Singanamalli
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Publication number: 20220392616Abstract: Systems and techniques that facilitate hybrid unsupervised and supervised image segmentation are provided. In various embodiments, a system can access a computed tomography (CT) image depicting an anatomical structure. In various aspects, the system can generate, via an unsupervised modeling technique, at least one class probability mask of the anatomical structure based on the CT image. In various instances, the system can generate, via a deep-learning model, an image segmentation based on the CT image and based on the at least one class probability mask.Type: ApplicationFiled: June 8, 2021Publication date: December 8, 2022Inventors: Soumya Ghose, Jhimli Mitra, Peter M Edic, Prem Venugopal, Jed Douglas Pack
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Patent number: 11304683Abstract: The subject matter discussed herein relates to multi-modal image alignment to facilitate biopsy procedures and post-biopsy procedures. In one such example, prostate structures (or other suitable anatomic features or structures) are automatically segmented in pre-biopsy MR and pre-biopsy ultrasound images. Thereafter, pre-biopsy MR and pre-biopsy ultrasound contours are aligned. To account for non-linear deformation of the imaged anatomic structure, a patient-specific transformation model is trained via deep learning based at least in part on the pre-biopsy ultrasound images. The pre-biopsy ultrasound images that are overlaid with the pre-biopsy MR contours and based off the deformable transformation model are then aligned with the biopsy ultrasound images. Such real-time alignment using multi-modality imaging techniques provides guidance during the biopsy and post-biopsy system.Type: GrantFiled: September 13, 2019Date of Patent: April 19, 2022Assignee: General Electric CompanyInventors: Jhimli Mitra, Thomas Kwok-Fah Foo, Desmond Teck Beng Yeo, David Martin Mills, Soumya Ghose, Michael John MacDonald
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Publication number: 20210251611Abstract: The present disclosure relates to automatically determining respiratory phases (e.g., end-inspiration/expiration respiratory phases) in real time using ultrasound beamspace data. The respiratory phases may be used subsequently in a therapy or treatment (e.g., image-guided radiation-therapy (IGRT)) for precise dose-delivery. In certain implementations, vessel bifurcation may be tracked and respiration phases determined in real time using the tracked vessel bifurcations to facilitate respiration gating of the treatment or therapy.Type: ApplicationFiled: February 19, 2020Publication date: August 19, 2021Inventors: Jhimli Mitra, Sudhanya Chatterjee, Thomas Kwok-Fah Foo, Desmond Teck Beng Yeo, Bryan Patrick Bednarz, Sydney Jupitz
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Patent number: 10957010Abstract: The subject matter discussed herein relates to the automatic, real-time registration of pre-operative magnetic resonance imaging (MRI) data to intra-operative ultrasound (US) data (e.g., reconstructed images or unreconstructed data), such as to facilitate surgical guidance or other interventional procedures. In one such example, brain structures (or other suitable anatomic features or structures) are automatically segmented in pre-operative and intra-operative ultrasound data. Thereafter, anatomic structure (e.g., brain structure) guided registration is applied between pre-operative and intra-operative ultrasound data to account for non-linear deformation of the imaged anatomic structure. MR images that are pre-registered to pre-operative ultrasound images are then given the same nonlinear spatial transformation to align the MR images with intra-operative ultrasound images to provide surgical guidance.Type: GrantFiled: August 7, 2019Date of Patent: March 23, 2021Assignee: General Electric CompanyInventors: Soumya Ghose, Jhimli Mitra, David Martin Mills, Lowell Scott Smith, Desmond Teck Beng Yeo, Thomas Kwok-Fah Foo
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Publication number: 20210077077Abstract: The subject matter discussed herein relates to multi-modal image alignment to facilitate biopsy procedures and post-biopsy procedures. In one such example, prostate structures (or other suitable anatomic features or structures) are automatically segmented in pre-biopsy MR and pre-biopsy ultrasound images. Thereafter, pre-biopsy MR and pre-biopsy ultrasound contours are aligned. To account for non-linear deformation of the imaged anatomic structure, a patient-specific transformation model is trained via deep learning based at least in part on the pre-biopsy ultrasound images. The pre-biopsy ultrasound images that are overlaid with the pre-biopsy MR contours and based off the deformable transformation model are then aligned with the biopsy ultrasound images. Such real-time alignment using multi-modality imaging techniques provides guidance during the biopsy and post-biopsy system.Type: ApplicationFiled: September 13, 2019Publication date: March 18, 2021Inventors: Jhimli Mitra, Thomas Kwok-Fah Foo, Desmond Teck Beng Yeo, David Martin Mills, Soumya Ghose, Michael John MacDonald
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Patent number: 10921395Abstract: A system and method for providing virtual real-time MRI-guidance for a biopsy outside of a conventional MRI scanner is described. MR images and ultrasound images of a region of a patient's body are simultaneously acquired during a pre-biopsy procedure. Respiratory states that the patient may experience during the biopsy are then determined from the acquired ultrasound images, and each respiratory state is associated with corresponding MR images. The MR images are indexed with their corresponding respiratory state. Ultrasound images are then acquired of the patient during a biopsy procedure. The respiratory state of the patient is determined from the ultrasound images, and the corresponding indexed MR images are displayed.Type: GrantFiled: January 12, 2018Date of Patent: February 16, 2021Assignee: GE PRECISION HEALTHCARE LLCInventors: Thomas Kwok-Fah Foo, Jhimli Mitra, Bo Wang, Lowell Scott Smith, David Martin Mills, Warren Lee, James Hartman Holmes, Bryan Bednarz, Roberta Marie Strigel
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Publication number: 20210042878Abstract: The subject matter discussed herein relates to the automatic, real-time registration of pre-operative magnetic resonance imaging (MRI) data to intra-operative ultrasound (US) data (e.g., reconstructed images or unreconstructed data), such as to facilitate surgical guidance or other interventional procedures. In one such example, brain structures (or other suitable anatomic features or structures) are automatically segmented in pre-operative and intra-operative ultrasound data. Thereafter, anatomic structure (e.g., brain structure) guided registration is applied between pre-operative and intra-operative ultrasound data to account for non-linear deformation of the imaged anatomic structure. MR images that are pre-registered to pre-operative ultrasound images are then given the same nonlinear spatial transformation to align the MR images with intra-operative ultrasound images to provide surgical guidance.Type: ApplicationFiled: August 7, 2019Publication date: February 11, 2021Inventors: Soumya Ghose, Jhimli Mitra, David Martin Mills, Lowell Scott Smith, Desmond Teck Beng Yeo, Thomas Kwok-Fah Foo
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Patent number: 10842445Abstract: A method is provided. The method includes acquiring simultaneously multiple magnetic resonance (MR) images and multiple ultrasound images of an anatomical region of a subject over a scanned duration. The method also includes training an unsupervised deep learning-based deformable registration network. This training includes training a MR registration subnetwork based on the multiple MR images to generate MR deformation and transformation vectors, training an ultrasound registration subnetwork based on the multiple ultrasound images to generate ultrasound deformation and transformation vectors, and training a MR-to-ultrasound subnetwork based the multiple MR images and the multiple ultrasound images to generate MR-to-ultrasound deformation and transformation vectors between corresponding pairs of MR images and ultrasound images at each time point.Type: GrantFiled: November 8, 2018Date of Patent: November 24, 2020Assignee: GENERAL ELECTRIC COMPANYInventors: Bo Wang, Thomas Kwok-Fah Foo, Desmond Teck Beng Yeo, Jhimli Mitra
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Publication number: 20200297219Abstract: In accordance with the present disclosure, deep-learning techniques are employed to find anomalies corresponding to bleed events. By way of example, a deep convolutional neural network or combination of such networks may be trained to determine the location of a bleed event, such as an internal bleed event, based on ultrasound data acquired at one or more locations on a patient anatomy. Such a technique may be useful in non-clinical settings.Type: ApplicationFiled: March 18, 2019Publication date: September 24, 2020Inventors: Jhimli Mitra, Luca Marinelli, Asha Singanamalli
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Publication number: 20200146635Abstract: A method is provided. The method includes acquiring simultaneously multiple magnetic resonance (MR) images and multiple ultrasound images of an anatomical region of a subject over a scanned duration. The method also includes training an unsupervised deep learning-based deformable registration network. This training includes training a MR registration subnetwork based on the multiple MR images to generate MR deformation and transformation vectors, training an ultrasound registration subnetwork based on the multiple ultrasound images to generate ultrasound deformation and transformation vectors, and training a MR-to-ultrasound subnetwork based the multiple MR images and the multiple ultrasound images to generate MR-to-ultrasound deformation and transformation vectors between corresponding pairs of MR images and ultrasound images at each time point.Type: ApplicationFiled: November 8, 2018Publication date: May 14, 2020Inventors: Bo Wang, Thomas Kwok-Fah Foo, Desmond Teck Beng Yeo, Jhimli Mitra
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Patent number: 10614567Abstract: Methods and apparatus quantify mass effect deformation in diagnostic images of patients demonstrating glioblastoma multiforme (GBM). One example apparatus includes an image acquisition circuit that acquires an image of a region of tissue demonstrating GBM pathology, a delineation circuit that segments a tumor region from the image, a pre-processing circuit that generates a pre-processed image by pre-processing the segmented image, a registration circuit that registers the pre-processed image with a template image of a healthy brain, a deformation quantification circuit that computes a set of differences between a position of a brain sub-structure represented in the registered image relative to the position of the brain sub-structure represented in the template image. Embodiments may include a classification circuit that classifies the region of tissue as a long or short-term survivor based, at least in part, on the set of differences.Type: GrantFiled: January 4, 2017Date of Patent: April 7, 2020Assignee: Case Western Reserve UniversityInventors: Pallavi Tiwari, Anant Madabhushi, Gavin Hanson, Jhimli Mitra
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Publication number: 20190219647Abstract: A system and method for providing virtual real-time MRI-guidance for a biopsy outside of a conventional MRI scanner is described. MR images and ultrasound images of a region of a patient's body are simultaneously acquired during a pre-biopsy procedure. Respiratory states that the patient may experience during the biopsy are then determined from the acquired ultrasound images, and each respiratory state is associated with corresponding MR images. The MR images are indexed with their corresponding respiratory state. Ultrasound images are then acquired of the patient during a biopsy procedure. The respiratory state of the patient is determined from the ultrasound images, and the corresponding indexed MR images are displayed.Type: ApplicationFiled: January 12, 2018Publication date: July 18, 2019Inventors: Thomas Kwok-Fah Foo, Jhimli Mitra, Bo Wang, Lowell Scott Smith, David Martin Mills, Warren Lee, James Hartman Holmes, Bryan Bednarz, Roberta Marie Strigel
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Publication number: 20180025489Abstract: Methods and apparatus quantify mass effect deformation in diagnostic images of patients demonstrating glioblastoma multiforme (GBM). One example apparatus includes an image acquisition circuit that acquires an image of a region of tissue demonstrating GBM pathology, a delineation circuit that segments a tumor region from the image, a pre-processing circuit that generates a pre-processed image by pre-processing the segmented image, a registration circuit that registers the pre-processed image with a template image of a healthy brain, a deformation quantification circuit that computes a set of differences between a position of a brain sub-structure represented in the registered image relative to the position of the brain sub-structure represented in the template image. Embodiments may include a classification circuit that classifies the region of tissue as a long or short-term survivor based, at least in part, on the set of differences.Type: ApplicationFiled: January 4, 2017Publication date: January 25, 2018Inventors: Pallavi Tiwari, Anant Madabhushi, Gavin Hanson, Jhimli Mitra