公开(公告)号：US11328394B1

公开(公告)日：2022-05-10

申请号：US17380429

申请日：2021-07-20

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 ,  G06T5/50 ,  G06N20/00

Abstract: Provided is a deep learning based contrast-enhanced (CE) CT image contrast amplifying method and the deep learning based CE CT image contrast amplifying method includes extracting at least one component CT image between a CE component and a non-CE component for an input CE CT image with the input CE CT image as an input to a previously trained deep learning model; and outputting a contrast-amplified CT image with respect to the CE CT image based on the input CE CT image and the at least one extracted component CT image.

公开(公告)号：US10891762B2

公开(公告)日：2021-01-12

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：US20250022130A1

公开(公告)日：2025-01-16

申请号：US18771565

申请日：2024-07-12

Applicant: Claripi Inc.

Inventor： Dong Ok KIM ,  Jong Hyo Kim

IPC: G06T7/00

Abstract: According to the disclosure, the image generating module generates a second multi-slice medical image set having a low resolution in a preset direction by reconstructing a first multi-slice medical image set received from an outside to have a cutting plane perpendicular to a slice plane in the preset direction, generates a third multi-slice medical image set, of which a resolution is improved as much as an integer multiple in the preset direction in slice images included in the second multi-slice medical image set through a deep learning model trained in advance by inputting the second multi-slice medical image set to the deep learning model, and generates and outputs a 3D high-resolution image improved in resolution based on the third multi-slice medical image set.