Abstract: Lower limb bone deformities have always been a common and difficult to treat disease in orthopedic medicine.Doctors usually need to judge the degree of deformities based on the patient's lower limb bone anteroposterior and lateral X-ray images.Its diagnosis and surgical plan design highly rely on the professional level and experience level of doctors,which is a very important challenge in the current medical field.In order to reduce the difficulty of diagnosis for doctors and provide them with more intuitive and accurate models of lower limb bone deformities,this study applies artificial intelligence deep learning technology to medical image processing and 3D reconstruction,and proposes the PSSobel-X2CTGAN model to achieve reconstruction from 2D X-ray films to 3D CT images.The main research content includes:1)investigating and organizing the data preprocessing processes of CT image normalization,cropping and scaling,and DRR generation,so as to better apply them to the training and prediction of 3D reconstruction models;2)apply the generative adversarial principle to model training,and optimize the sampling process on the generator to make the generated 3D model closer to the real situation;3)design a reasonable loss function,based on the basic reconstruction and projection losses,and introduce sobel loss to make the edges of the final image clearer,making it more suitable for high-precision 3D bone model reconstruction.Experiments are conducted on open-source pelvic and knee joint data,and the results showed that this model outperformed the original model in various evaluation indicators.Moreover,from the visual image results,the model can achieve satisfactory results and has practical value for the diagnosis of lower limb deformities.

Key words: Lower limb deformity correction, 3D reconstruction, Medical imaging processing, Generative adversarial network