The present disclosure relates to a model training method, an image reconstruction method, and a related device. A computer device acquires a pre-trained target neural network model, the target neural network model being obtained by training a plurality of groups of training samples by means of a machine learning algorithm, and each group of training samples comprising a CTP image and a CTA image which are paired with each other, wherein the CTP image and the CTA image which are paired with each other correspond to the same selected level of target tissue; a target CTP image is input into the target neural network model, such that the target neural network model reconstructs a target CTA image on the basis of the target CTP image. The blood vessel imaging effect of the target CTA image is close to that of a real CTA image or is even better that that of the real CTA image, thereby providing a real and accurate reference basis for examination of each tissue and organ, and a patient can undergo one less CTA examination, thereby increasing the speed of one-stop CT examination for stroke, avoiding exposure to radiation of one more CTA examination, and reducing one injection of a contrast agent, lowering the risk of side effects generated by the contrast agent.
- 출원번호 : CN2024/123757
- 출원인 : SHANGHAI RADIODYNAMIC HEALTHCARE TECHNOLOGY CO., LTD.
- 특허번호 :
- IPC : G06T-011/00(2006.01);G06N-020/00(2019.01);