The present application relates to the technical field of
image generation, in particular to a method and
system for generating a
lung DR image into a CT
respiratory motion image, the method of the present application first acquires DR data and CT data of multiple patients, forms a generalization
data set after preprocessing, trains a
generative adversarial network to obtain a generalization
generative adversarial network; then, using the multi-phase CT data and DR data of a historical
respiratory cycle of a target patient before surgical intervention, a specific
data set is obtained, the generalization
generative adversarial network is trained to obtain a specific generative
adversarial network suitable for the target patient, which greatly reduces the generation deviation caused by individual differences and improves the matching degree of the generated image and the real physiological state of the patient; finally, during the interventional
surgery and radiotherapy, only the real-time DR data of the
lung of the target patient needs to be obtained, and the real-time 4D CT data of the patient can be obtained, so that the
lesion positioning and targeted treatment are more timely and accurate.