公开(公告)号：US20230214972A1

公开(公告)日：2023-07-06

申请号：US18148267

申请日：2022-12-29

Applicant: CLARIPI INC.

Inventor： Hyun Sook PARK ,  Tae Jin KIM ,  Chul Kyun AHN

IPC: G06T5/20 ,  G06T5/00 ,  G06T7/20

CPC classification number: G06T5/20 ,  G06T5/002 ,  G06T7/20 ,  G06T2207/20021 ,  G06T2207/20081 ,  G06T2207/20221 ,  G06T2207/20224 ,  G06T2207/30004

Abstract: Disclosed are a motion compensation processing apparatus and method of medical images, in which motion of organs is corrected, the method including: acquiring the medical image and combining an organ motion component into the medical image; training at least one deep learning model based on the medical image combined with the organ motion component so that the deep learning model can remove the organ motion component; and acquiring a processing medical image, selecting a deep learning model corresponding to an organ included in the processing medical image, and removing a motion component for the organ.

公开(公告)号：US20220036512A1

公开(公告)日：2022-02-03

申请号：US17505159

申请日：2021-10-19

Applicant: ClariPI Inc. ,  Seoul National University R&DB Foundation

Inventor： Jong Hyo KIM ,  Hyun Sook PARK ,  Tai Chul PARK ,  Chul Kyun AHN

IPC: G06T5/00 ,  G01R33/56 ,  G01R33/565 ,  G01R33/48

Abstract: Provided is a deep learning based accelerated MRI image quality restoring method. The deep learning based accelerated MRI image quality restoring method includes extracting test information from an input accelerated MRI image, selecting at least one deep learning model corresponding to the test information, among a plurality of previously trained deep learning models, and outputting an MRI image with a restored image quality for the input accelerated MRI image with the input accelerated MRI image as an input of at least one selected deep learning model.