Super-Resolution Reconstruction and Inpainting of Inter-Layer Image in 3D CT

CT image plays an important role in the diagnosis of diseases and precise radiotherapy of cancer.However,due to the limitation of X-ray dose and the existing medical equipment,the sampling interval of 3D CT image is large,which results in the much lower inter-layer resolution of 3D CT images than the intra-layer resolution.In order to improve the inter-layer resolution and avoid the interference with super-resolution reconstruction from the large difference between adjacent layers,this paper proposes a new model for reconstructing an intermediate slice that combines the registration information from adjacent CT sequence,and then repairer the new slice via non-local self-similarity features.First,CLG-TV optical flow estimation model is used to estimate the motion between two adjacent CT slices,for obtaining a dense optical flow field.The model constructs the precise pixel correspondence between two consecutive slices,which can scale the velocity vector according to the position of new slice.Then,an initial interpolated image based on the calculated velocity field is generated.Since it is difficult to guarantee the one-to-one pixel correspondence between original adjacent slices,there generally exists the pixel missing phenomenon in interpolated images.Finally,non-local self-similarity among different frames is used to inpaint the missing regions in the interpolated image.The experimental results on the data sets provided by DIR-Lab laboratory and Shandong Medical Imaging Research Institute show that,compared with other classic methods,this method can generate high quality intermediate CT slices,and improve the inter-layer resolution quantitatively and qualitatively.