Korea Institute of Radiological & Medical Sciences

Sang Keun Woo

Sang Moo Lim

Ji Min Kim

Kyo Chul Lee

Kyeong Min Kim

Tae Won Nam

Wook Kim

The present invention relates to a radiopharmaceutical distribution image generation system and method using deep learning and, more specifically, to a radiopharmaceutical distribution image generation system and method using deep learning, wherein dynamic medical images collected from multiple patients and a time-radiation dose distribution curve for each organ can be learnt through a deep learning network and a spatial distribution image of a radiopharmaceutical can be generated from a static medical image acquired from a specific patient. According to the present invention, even when a medical image is acquired only for a specific time after a radiopharmaceutical is injected into a patient, a spatial distribution image of the radiopharmaceutical can be acquired across the entire time by using the deep learning network, and quantitative analysis of the radiopharmaceutical can be performed by calculating a time-radiation dose distribution curve on the basis thereof.

Radiopharmaceutical distribution image generation system and method using deep learning

Provided are apparatuses and methods of resampling event data for quantitative improvement of a PET image, which acquire a quantitatively-improved new PET image by analyzing a storage format of list data prior to conversion into the PET image and nonparametrically resampling event data from the list data based on the analysis results to improve noise and statistical characteristics thereof. The quantitatively improved new PET image may be acquired by analyzing a storage format of list data constituting the PET image and nonparametrically resampling event data to improve noise and statistical characteristics thereof.

Apparatus and method of resampling event data for quantitative improvement of pet image

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Seoul, South Korea

Sang Keun Woo