Claripi Inc.

Seoul National University

Chul Kyun Ahn

Hyun Sook Park

Tae Jin Kim

Jong Hyo Kim

Tai Chul Park

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.

Motion compensation processing apparatus and method of medical images

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.

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Seoul, South Korea

Chul Kyun Ahn