US 12,249,415 B2
Radiopharmaceutical distribution image generation system and method using deep learning
Sang Keun Woo, Seoul (KR); Sang Moo Lim, Seoul (KR); Kyo Chul Lee, Seoul (KR); and Wook Kim, Seongnam-si (KR)
Assigned to KOREA INSTITUTE OF RADIOLOGICAL & MEDICAL SCIENCES, Seoul (KR)
Appl. No. 17/787,845
Filed by KOREA INSTITUTE OF RADIOLOGICAL & MEDICAL SCIENCES, Seoul (KR)
PCT Filed Jul. 2, 2020, PCT No. PCT/KR2020/008658
§ 371(c)(1), (2) Date Jun. 21, 2022,
PCT Pub. No. WO2021/137371, PCT Pub. Date Jul. 8, 2021.
Claims priority of application No. 10-2020-0000523 (KR), filed on Jan. 2, 2020.
Prior Publication US 2023/0029695 A1, Feb. 2, 2023
Int. Cl. G16H 30/20 (2018.01); G06T 7/00 (2017.01); G06T 7/269 (2017.01); G16H 20/10 (2018.01); G16H 30/40 (2018.01)
CPC G16H 30/20 (2018.01) [G06T 7/0012 (2013.01); G06T 7/269 (2017.01); G16H 20/10 (2018.01); G16H 30/40 (2018.01); G06T 2207/20081 (2013.01)] 6 Claims
OG exemplary drawing
 
1. A radiopharmaceutical distribution image generation system using deep learning, the system comprising:
a dynamic medical image acquisition unit configured to acquire dynamic medical images by continuously collecting medical images of a patient concurrently with injection of radiopharmaceuticals;
a distribution curve acquisition unit configured to acquire a time-radiation dose distribution curve, which represents a radiation dose for each organ of a human body over time, from the dynamic medical images;
a static medical image acquisition unit configured to acquire static medical images for a specific period of time after the injection of the radiopharmaceuticals;
a deep-learning image generation network configured to predict and generate medical images corresponding to times before and after the static medical images by collecting and learning the dynamic medical images of multiple patients and the corresponding time-radiation dose distribution curve; and
a spatial distribution image acquisition unit configured to acquire a spatial distribution image of the radiopharmaceuticals from the static medical images and the generated medical images,
wherein the deep learning network comprises:
a deep learning unit configured to collect and learn the dynamic medical images of the multiple patients and the corresponding time-radiation dose distribution curve; and
an image generation unit configured to predict and generate the medical images corresponding to times before and after the static medical images are taken.