公开(公告)号：US10909681B2

公开(公告)日：2021-02-02

申请号：US16239303

申请日：2019-01-03

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Albert Hsiao ,  Naeim Bahrami ,  Tara Retson

IPC: G06T7/00 ,  G06K9/62

Abstract: A method for identification of an optimal image within a sequence of image frames includes inputting the sequence of images into a computer processor configured for executing a plurality of neural networks and applying a sliding window to the image sequence to identify a plurality of image frame windows. The image frame windows are processed using a first neural network trained to classify the image frames according to identified spatial features. The image frame windows are also processed using a second neural network trained to classify the image frames according to identified serial features. The results of each classification are concatenated to separate each of the image frame windows into one of two classes, one class containing the optimal image. An output is generated to display image frame windows classification as including the optimal image.

公开(公告)号：US12260549B2

公开(公告)日：2025-03-25

申请号：US17672613

申请日：2022-02-15

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Albert Hsiao ,  Evan Masutani ,  Sophie You

IPC: G06T7/00 ,  G06N3/08 ,  G06T7/20 ,  G06V10/82

Abstract: A method and system for automated correction of phase error in MRI-based flow evaluation employs a computer processor programmed to execute a trained convolutional neural network (CNN) to receive and process image data comprising flow velocity data in three directions and magnitude data collected from a region of interest over a scan period from magnetic resonance imaging instrumentation. The image data is processed using the trained CNN to generate three output channels with pixelwise inferred corrections for the flow velocity data which are further smoothed using a regression algorithm. The smoothed corrections are added to the original image data to generate corrected flow data, which may be used for flow visualization and quantization.

公开(公告)号：US20220114699A1

公开(公告)日：2022-04-14

申请号：US17498288

申请日：2021-10-11

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Albert Hsiao ,  Evan MASUTANI

IPC: G06T3/40 ,  G06N3/02 ,  G16H30/40

Abstract: A method for improving resolution of a lower resolution image includes inputting at least one digital image into a convolutional neural network (CNN) comprising one of a SRNet and a UNet to output a two-dimensional image having a higher resolution. In some embodiments, multiple image frames may be processed using a 3D CNN to generate a two-dimensional output image.

公开(公告)号：US20200219262A1

公开(公告)日：2020-07-09

申请号：US16239303

申请日：2019-01-03

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Albert Hsiao ,  Naeim Bahrami ,  Tara Retson

IPC: G06T7/00 ,  G06K9/62

Abstract: A method for identification of an optimal image within a sequence of image frames includes inputting the sequence of images into a computer processor configured for executing a plurality of neural networks and applying a sliding window to the image sequence to identify a plurality of image frame windows. The image frame windows are processed using a first neural network trained to classify the image frames according to identified spatial features. The image frame windows are also processed using a second neural network trained to classify the image frames according to identified serial features. The results of each classification are concatenated to separate each of the image frame windows into one of two classes, one class containing the optimal image. An output is generated to display image frame windows classification as including the optimal image.