Disclosed in the present invention is a method for enhancing the quality and resolution of CT images based on deep learning, comprising the following steps: S1: pre-processing collected clinical data to obtain a data set; S2: building a deep learning model comprising a generative network, a decider network, and a cognitive network; S3: building a loss function; S4: using the data set and the loss function to update the parameters of the iterative generative network in order to obtain a trained deep learning model; and S5: inputting a low-quality low-resolution image into the trained deep learning model to obtain a high-quality high-resolution image. The present invention builds a deep learning model based on deep learning and pre-processes clinical data to obtain a data set, reducing the impact of spatial misalignment of data collected at different times due to movement of the patient or other reasons; by means of the deep learning model combined with the loss function, end-to-end processing of the two tasks of enhancing CT image quality and super-resolution can be implemented to directly obtain final results.
METHOD FOR ENHANCING QUALITY AND RESOLUTION OF CT IMAGES BASED ON DEEP LEARNING