公开(公告)号：US20230237661A1

公开(公告)日：2023-07-27

申请号：US18100525

申请日：2023-01-23

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Albert HSIAO ,  Kyle HASENSTAB

IPC: G06T7/00 ,  G06T7/30 ,  G06V10/82 ,  G06V10/75

CPC classification number: G06T7/0016 ,  G06T7/30 ,  G06V10/82 ,  G06V10/754 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30061 ,  G06V2201/031

Abstract: A method and system for automated deformable registration of an organ from medical images includes generating segmentations of the organ by processing a first and second series of images corresponding to different organ states using a first trained CNN. A second trained CNN processes the first and second series of images and the segmentations to deformably register the second series of images to the first series of images. The second trained CNN predicts a displacement field by minimizing a registration loss function, where the displacement field maximizes colocalization of the organ between the different states.

公开(公告)号：US20230147286A1

公开(公告)日：2023-05-11

申请号：US17984146

申请日：2022-11-09

Applicant: The Regents of the University of California

Inventor： Albert HSIAO ,  Evan MASUTANI

IPC: G06T7/00 ,  G06T7/10 ,  G06T7/269 ,  G06V10/771 ,  G06V10/82

CPC classification number: G06T7/0012 ,  G06T7/10 ,  G06T7/269 ,  G06V10/771 ,  G06V10/82 ,  G06T2207/30048

Abstract: A neural network architecture and method for analysis of time series images from an image source employs a 3D-UNet convolutional neural network (CNN) configured to receive the time series images and generate spatiotemporal feature maps therefrom. Multiple sub-convolutional neural network output prongs based on an SRNet architecture receive the feature maps and simultaneously generate inferences for image segmentation, regression of values, and multi-landmark localization.

公开(公告)号：US20220261991A1

公开(公告)日：2022-08-18

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 ,  G06T7/20 ,  G06N3/08

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.