Patents by Inventor Jo Schlemper

Jo Schlemper 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: 12181553
    Abstract: A magnetic resonance imaging (MRI) system, comprising: a magnetics system comprising: a B0 magnet configured to provide a B0 field for the MRI system; gradient coils configured to provide gradient fields for the MRI system; and at least one RF coil configured to detect magnetic resonance (MR) signals; and a controller configured to: control the magnetics system to acquire MR spatial frequency data using non-Cartesian sampling; and generate an MR image from the acquired MR spatial frequency data using a neural network model comprising one or more neural network blocks including a first neural network block, wherein the first neural network block is configured to perform data consistency processing using a non-uniform Fourier transformation.
    Type: Grant
    Filed: March 23, 2022
    Date of Patent: December 31, 2024
    Assignee: Hyperfine Operations, Inc.
    Inventors: Jo Schlemper, Seyed Sadegh Mohseni Salehi, Michal Sofka, Prantik Kundu, Ziyi Wang, Carole Lazarus, Hadrien A. Dyvorne, Laura Sacolick, Rafael O'Halloran, Jonathan M. Rothberg
  • Patent number: 12105173
    Abstract: Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.
    Type: Grant
    Filed: May 5, 2023
    Date of Patent: October 1, 2024
    Assignee: Hyperfine Operations, Inc.
    Inventors: Jo Schlemper, Seyed Sadegh Mohseni Salehi, Michal Sofka
  • Publication number: 20240290011
    Abstract: Systems and methods for dual-domain self-supervised learning for accelerated non-Cartesian magnetic resonance imaging reconstruction are provided. The present techniques provide a method for training a machine-learning model that receives magnetic resonance (MR) data and generates a reconstruction of the MR data. The machine-learning model can be trained based on a set of losses comprising a first loss value corresponding to a frequency-domain and a second loss value corresponding to an image-based domain. The training process can be a self-supervised training process that can utilize under-sampled and non-Cartesian MR data. The machine-learning model is trained by optimizing both data consistency in the frequency domain and appearance consistency in the image-based domain.
    Type: Application
    Filed: March 6, 2024
    Publication date: August 29, 2024
    Applicant: Hyperfine, Inc.
    Inventors: Bo Zhou, Jo Schlemper, Neel Dey, Michal Sofka
  • Publication number: 20240257366
    Abstract: A computer-implemented method that includes providing as input to the neural network, a first image and a second image. The method further includes obtaining, using the neural network, a first transformed image based on the first image that may be aligned with the second image. The method further includes computing a first loss value based on a comparison of the first transformed image and the second image. The method further includes obtaining, using the neural network, a second transformed image based on the second image that may be aligned with the first image. The method further includes computing a second loss value based on a comparison of the second transformed image and the first image. The method further includes adjusting one or more parameters of the neural network based on the first loss value and the second loss value.
    Type: Application
    Filed: March 20, 2024
    Publication date: August 1, 2024
    Applicant: Hyperfine Operations, Inc.
    Inventors: Neel Dey, Jo Schlemper, Seyed Sadegh Mohseni Salehi, Li Yao, Michal Sofka
  • Publication number: 20240233148
    Abstract: A computer-implemented method that includes providing as input to the neural network, a first image and a second image. The method further includes obtaining, using the neural network, a transformed image based on the first image that may be aligned with the second image. The method further includes obtaining a plurality of first patches from the transformed image by encoding the transformed image using a first encoder that has a first plurality of encoding layers. The method further includes obtaining a plurality of second patches from the second image by encoding the second image using a second encoder that has a second plurality of encoding layers. The method further includes computing a loss value based on comparison of respective first patches and second patches. The method further includes adjusting one or more parameters of the neural network based on the loss value.
    Type: Application
    Filed: March 20, 2024
    Publication date: July 11, 2024
    Applicant: Hyperfine Operations, Inc.
    Inventors: Neel Dey, Jo Schlemper, Seyed Sadegh Mohseni Salehi, Li Yao, Michal Sofka
  • Publication number: 20240118359
    Abstract: Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.
    Type: Application
    Filed: May 5, 2023
    Publication date: April 11, 2024
    Applicant: Hyperfine Operations, Inc.
    Inventors: Jo Schlemper, Seyed Sadegh Mohseni Salehi, Michal Sofka
  • Publication number: 20230417852
    Abstract: Techniques for removing artefacts, such as RF interference and/or noise, from magnetic resonance data. The techniques include: obtaining input magnetic resonance (MR) data using at least one radio-frequency (RF) coil of a magnetic resonance imaging (MRI) system; and generating an MR image from input MR data at least in part by using a neural network model to suppress at least one artefact in the input MR data.
    Type: Application
    Filed: September 12, 2023
    Publication date: December 28, 2023
    Applicant: Hyperfine Operations, Inc.
    Inventors: Carole LAZARUS, Prantik KUNDU, Sunli TANG, Seyed Sadegh Mohseni SALEHI, Michal SOFKA, Jo SCHLEMPER, Hadrien A. DYVORNE, Rafael O'HALLORAN, Laura SACOLICK, Michael Stephen POOLE, Jonathan M. ROTHBERG
  • Patent number: 11789104
    Abstract: Techniques for removing artefacts, such as RF interference and/or noise, from magnetic resonance data. The techniques include: obtaining input magnetic resonance (MR) data using at least one radio-frequency (RF) coil of a magnetic resonance imaging (MRI) system; and generating an MR image from input MR data at least in part by using a neural network model to suppress at least one artefact in the input MR data.
    Type: Grant
    Filed: August 15, 2019
    Date of Patent: October 17, 2023
    Assignee: Hyperfine Operations, Inc.
    Inventors: Carole Lazarus, Prantik Kundu, Sunli Tang, Seyed Sadegh Mohseni Salehi, Michal Sofka, Jo Schlemper, Hadrien A. Dyvorne, Rafael O'Halloran, Laura Sacolick, Michael Stephen Poole, Jonathan M. Rothberg
  • Patent number: 11681000
    Abstract: Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.
    Type: Grant
    Filed: September 17, 2021
    Date of Patent: June 20, 2023
    Assignee: Hyperfine Operations, Inc.
    Inventors: Jo Schlemper, Seyed Sadegh Mohseni Salehi, Michal Sofka
  • Patent number: 11564590
    Abstract: Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques include: obtaining input MR spatial frequency data obtained by imaging the subject using the MRI system; generating an MR image of the subject from the input MR spatial frequency data using a neural network model comprising: a pre-reconstruction neural network configured to process the input MR spatial frequency data; a reconstruction neural network configured to generate at least one initial image of the subject from output of the pre-reconstruction neural network; and a post-reconstruction neural network configured to generate the MR image of the subject from the at least one initial image of the subject.
    Type: Grant
    Filed: March 12, 2020
    Date of Patent: January 31, 2023
    Assignee: Hyperfine Operations, Inc.
    Inventors: Jo Schlemper, Seyed Sadegh Mohseni Salehi, Michal Sofka, Prantik Kundu, Carole Lazarus, Hadrien A. Dyvorne, Rafael O'Halloran, Laura Sacolick
  • Patent number: 11467239
    Abstract: A magnetic resonance imaging (MRI) system, comprising: a magnetics system comprising: a B0 magnet configured to provide a B0 field for the MRI system; gradient coils configured to provide gradient fields for the MRI system; and at least one RF coil configured to detect magnetic resonance (MR) signals; and a controller configured to: control the magnetics system to acquire MR spatial frequency data using non-Cartesian sampling; and generate an MR image from the acquired MR spatial frequency data using a neural network model comprising one or more neural network blocks including a first neural network block, wherein the first neural network block is configured to perform data consistency processing using a non-uniform Fourier transformation.
    Type: Grant
    Filed: July 29, 2019
    Date of Patent: October 11, 2022
    Assignee: Hyperfine Operations, Inc.
    Inventors: Jo Schlemper, Seyed Sadegh Mosheni Salehi, Michal Sofka, Prantik Kundu, Ziyi Wang, Carole Lazarus, Hadrien A. Dyvorne, Laura Sacolick, Rafael O'Halloran, Jonathan M. Rothberg
  • Publication number: 20220214417
    Abstract: A magnetic resonance imaging (MRI) system, comprising: a magnetics system comprising: a B0 magnet configured to provide a B0 field for the MRI system; gradient coils configured to provide gradient fields for the MRI system; and at least one RF coil configured. to detect magnetic resonance (MR) signals; and a controller configured to: control the magnetics system to acquire MR spatial frequency data using non-Cartesian sampling; and generate an MR image from the acquired MR spatial frequency data using a neural network model comprising one or more neural network blocks including a first neural network block, wherein the first neural network block is configured to perform data consistency processing using a non-uniform Fourier transformation.
    Type: Application
    Filed: March 23, 2022
    Publication date: July 7, 2022
    Applicant: Hyperfine Operations, Inc.
    Inventors: Jo Schlemper, Seyed Sadegh Mohseni Salehi, Michal Sofka, Prantik Kundu, Ziyi Wang, Carole Lazarus, Hadrien A. Dyvorne, Laura Sacolick, Rafael O'Halloran, Jonathan M. Rothberg
  • Patent number: 11344219
    Abstract: Generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system by: generating first and second sets of one or more MR images from first and second input MR data; aligning the first and second sets of MR images using a neural network model comprising first and second neural networks, the aligning comprising: estimating, using the first neural network, a first transformation between the first and second sets of MR images; generating a first updated set of MR images from the second set of MR images using the first transformation; estimating, using the second neural network, a second transformation between the first set and the first updated set of MR images; and aligning the first set of MR images and the second set of MR images at least in part by using the first transformation and the second transformation.
    Type: Grant
    Filed: March 12, 2020
    Date of Patent: May 31, 2022
    Assignee: Hyperfine Operations, Inc.
    Inventors: Jo Schlemper, Seyed Sadegh Mosheni Salehi, Michal Sofka
  • Patent number: 11324418
    Abstract: Techniques for generating magnetic resonance (MR) images from MR data obtained by a magnetic resonance imaging (MRI) system comprising a plurality of RF coils configured to detect RF signals. The techniques include: obtaining a plurality of input MR datasets obtained by the MRI system to image a subject, each of the plurality of input MR datasets comprising spatial frequency data and obtained using a respective RF coil in the plurality of RF coils; generating a respective plurality of MR images from the plurality of input MR datasets by using an MR image reconstruction technique; estimating, using a neural network model, a plurality of RF coil profiles corresponding to the plurality of RF coils; generating an MR image of the subject using the plurality of MR images and the plurality of RF coil profiles; and outputting the generated MR image.
    Type: Grant
    Filed: March 12, 2020
    Date of Patent: May 10, 2022
    Assignee: Hyperfine Operations, Inc.
    Inventors: Jo Schlemper, Seyed Sadegh Moshen Salehi, Michal Sofka
  • Patent number: 11300645
    Abstract: A magnetic resonance imaging (MRI) system, comprising: a magnetics system comprising: a B0 magnet configured to provide a B0 field for the MRI system; gradient coils configured to provide gradient fields for the MRI system; and at least one RF coil configured to detect magnetic resonance (MR) signals; and a controller configured to: control the magnetics system to acquire MR spatial frequency data using non-Cartesian sampling; and generate an MR image from the acquired MR spatial frequency data using a neural network model comprising one or more neural network blocks including a first neural network block, wherein the first neural network block is configured to perform data consistency processing using a non-uniform Fourier transformation.
    Type: Grant
    Filed: July 29, 2019
    Date of Patent: April 12, 2022
    Assignee: Hyperfine Operations, Inc.
    Inventors: Jo Schlemper, Seyed Sadegh Moshen Salehi, Michal Sofka, Prantik Kundu, Ziyi Wang, Carole Lazarus, Hadrien A. Dyvorne, Laura Sacolick, Rafael O'Halloran, Jonathan M. Rothberg
  • Publication number: 20220107378
    Abstract: Techniques for denoising a magnetic resonance (MR) image are provided, including: obtaining a noisy MR image; denoising the noisy MR image of the subject using a denoising neural network model, and outputting a denoised MR image. The denoising neural network model is trained by: generating first training data for training a first neural network model to denoise MR images by generating a first plurality of noisy MR images using clean MR data associated with a source domain and first MR noise data associated with the target domain; training the first neural network model using the first training data; generating training data for training the denoising neural network model by applying the first neural network model to a second plurality of noisy MR images and generating a plurality of denoised MR images; and training the denoising neural network model using the training data for training the denoising neural network model.
    Type: Application
    Filed: October 7, 2021
    Publication date: April 7, 2022
    Inventors: Neel Dey, Jo Schlemper, Seyed Sadegh Moshen Salehi, Michal Sofka, Prantik Kundu
  • Publication number: 20220015662
    Abstract: Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.
    Type: Application
    Filed: September 17, 2021
    Publication date: January 20, 2022
    Applicant: Hyperfine, Inc.
    Inventors: Jo Schlemper, Seyed Sadegh Moshen Salehi, Michal Sofka
  • Patent number: 11185249
    Abstract: Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.
    Type: Grant
    Filed: March 12, 2020
    Date of Patent: November 30, 2021
    Assignee: Hyperfine, Inc.
    Inventors: Jo Schlemper, Seyed Sadegh Moshen Salehi, Michal Sofka
  • Publication number: 20200294229
    Abstract: Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.
    Type: Application
    Filed: March 12, 2020
    Publication date: September 17, 2020
    Inventors: Jo Schlemper, Seyed Sadegh Moshen Salehi, Michal Sofka
  • Publication number: 20200294287
    Abstract: Techniques for generating magnetic resonance (MR) images from MR data obtained by a magnetic resonance imaging (MRI) system comprising a plurality of RF coils configured to detect RF signals. The techniques include: obtaining a plurality of input MR datasets obtained by the MRI system to image a subject, each of the plurality of input MR datasets comprising spatial frequency data and obtained using a respective RF coil in the plurality of RF coils; generating a respective plurality of MR images from the plurality of input MR datasets by using an MR image reconstruction technique; estimating, using a neural network model, a plurality of RF coil profiles corresponding to the plurality of RF coils; generating an MR image of the subject using the plurality of MR images and the plurality of RF coil profiles; and outputting the generated MR image.
    Type: Application
    Filed: March 12, 2020
    Publication date: September 17, 2020
    Inventors: Jo Schlemper, Seyed Sadegh Moshen Salehi, Michal Sofka