US 12,408,881 B2
Radiography method, trained model, radiography module, radiography program, radiography system, and machine learning method
Tatsuya Onishi, Hamamatsu (JP); and Toshiyasu Suyama, Hamamatsu (JP)
Assigned to HAMAMATSU PHOTONICS K.K., Hamamatsu (JP)
Appl. No. 17/918,178
Filed by HAMAMATSU PHOTONICS K.K., Hamamatsu (JP)
PCT Filed Apr. 14, 2021, PCT No. PCT/JP2021/015488
§ 371(c)(1), (2) Date Oct. 11, 2022,
PCT Pub. No. WO2021/210617, PCT Pub. Date Oct. 21, 2021.
Claims priority of application No. 2020-073576 (JP), filed on Apr. 16, 2020.
Prior Publication US 2023/0125182 A1, Apr. 27, 2023
Int. Cl. A61B 6/00 (2024.01); A61B 6/42 (2024.01); G06T 5/70 (2024.01)
CPC A61B 6/42 (2013.01) [A61B 6/482 (2013.01); G06T 5/70 (2024.01); G06T 2207/10116 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20182 (2013.01)] 14 Claims
OG exemplary drawing
 
1. A radiographic image processing method comprising:
inputting condition information indicating either conditions of a source of radiation or imaging conditions when the radiation is radiated to capture an image of a target object;
calculating average energy related to the radiation passing through the target object on the basis of the condition information;
narrowing down candidates for a trained model from a plurality of trained models constructed through machine training in advance using image data on the basis of the average energy; and
executing image processing for removing noise from a radiographic image of the target object using the candidates.