公开(公告)号：US20190156524A1

公开(公告)日：2019-05-23

申请号：US16194941

申请日：2018-11-19

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

Inventor： Hyun Sook PARK ,  Jong Hyo KIM

IPC: G06T11/00 ,  G06T5/00 ,  G06T7/00

Abstract: Provided is a method for CT image denoising based on deep learning, and the method for CT image denoising based on deep learning includes: extracting examination information from an input CT image; selecting at least one deep learning model corresponding to the examination information from multiple previously trained deep learning models; and outputting a CT image denoised from the input CT image by feeding the input CT image into the selected at least one deep learning model.

公开(公告)号：US20230029394A1

公开(公告)日：2023-01-26

申请号：US17868314

申请日：2022-07-19

Applicant: CLARIPI INC.

Inventor： Hyun Sook PARK ,  Chang Yong HEO ,  Tae Jin KIM ,  Tae Yoon LIM ,  Je Myoung LEE

IPC: G06T7/00 ,  G06T3/40 ,  G16H30/40

Abstract: Disclosed are an apparatus and method for medical image processing according to pathologic lesion properties, the method including: recognizing a readout area different from an original readout area in a medical image by applying a previously trained deep learning model to the medical image, extracting properties, which include at least one of a location and a size of the readout area, from the medical image, and generating a readout image for the readout area, which is different from the original readout area corresponding to a purpose of taking the medical image, by reconstructing the medical image, thereby having an effect on generating a readout image for a different kind of pathologic lesion from a previously acquired medical image.

公开(公告)号：US20230215538A1

公开(公告)日：2023-07-06

申请号：US18148286

申请日：2022-12-29

Applicant: CLARIPI INC.

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

IPC: G16H20/17 ,  G06T7/00

CPC classification number: G16H20/17 ,  G06T7/0012 ,  G06T2207/10081

Abstract: Disclosed are an optimization method and system for a personalized contrast test based on deep learning, in which a contrast medium optimized for each individual patient is injected to implement optimum pharmacokinetic characteristics in a process of acquiring a medical image, the method including: obtaining drug information of a contrast medium and body information of a patient, in a contrast enhanced computed tomography (CT) scan; generating injection information of the drug to be injected into the patient by a predefined algorithm based on the drug information and the body information; injecting the drug into the patient based on the injection information, and acquiring a medical image by scanning the patient; and amplifying a contrast component in the medical image by inputting the medical image to a deep learning model trained in advance.

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.