Patents by Inventor Sydney KAPLAN

Sydney KAPLAN 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).

  • Full dose PET image estimation from low-dose PET imaging using deep learning
    Patent number: 11576628
    Abstract: Emission imaging data are reconstructed to generate a low dose reconstructed image. Standardized uptake value (SUV) conversion (30) is applied to convert the low dose reconstructed image to a low dose SUV image. A neural network (46, 48) is applied to the low dose SUV image to generate an estimated full dose SUV image. Prior to applying the neural network the low dose reconstructed image or the low dose SUV image is filtered using a low pass filter (32). The neural network is trained on a set of training low dose SUV images and corresponding training full dose SUV images to transform the training low dose SUV images to match the corresponding training full dose SUV images, using a loss function having a mean square error loss component (34) and a loss component (36) that penalizes loss of image texture and/or a loss component (38) that promotes edge preservation.
    Type: Grant
    Filed: December 26, 2018
    Date of Patent: February 14, 2023
    Assignee: KONINKLIJKE PHILIPS N.V.
    Inventors: Sydney Kaplan, Yang-Ming Zhu, Andriy Andreyev, Chuanyong Bai, Steven Michael Cochoff
  • FULL DOSE PET IMAGE ESTIMATION FROM LOW-DOSE PET IMAGING USING DEEP LEARNING
    Publication number: 20210052233
    Abstract: Emission imaging data are reconstructed to generate a low dose reconstructed image. Standardized uptake value (SUV) conversion (30) is applied to convert the low dose reconstructed image to a low dose SUV image. A neural network (46, 48) is applied to the low dose SUV image to generate an estimated full dose SUV image. Prior to applying the neural network the low dose reconstructed image or the low dose SUV image is filtered using a low pass filter (32). The neural network is trained on a set of training low dose SUV images and corresponding training full dose SUV images to transform the training low dose SUV images to match the corresponding training full dose SUV images, using a loss function having a mean square error loss component (34) and a loss component (36) that penalizes loss of image texture and/or a loss component (38) that promotes edge preservation.
    Type: Application
    Filed: December 26, 2018
    Publication date: February 25, 2021
    Inventors: Sydney KAPLAN, Yang-Ming ZHU, Andriy ANDREYEV, Chuanyong BAI, Steven Michael COCHOFF