Inter-Slice Resolution Improvement Using Convolutional Neural Network with Orbital Bone Edge-Aware in Facial CT Images

Abstract

The 3D modeling of orbital bones in facial CT images is essential to provide a customized implant for reconstructions of orbit and related structures during surgery. However, 3D models of the orbital bone show an aliasing effect and disconnected thin bone in the inter-slice direction because the slice thickness is two to three times larger than the pixel spacing. To improve the inter-slice resolution of facial CT images, we propose a method based on a 2D convolutional neural network (CNN) that uses the spatial information on the sagittal and axial planes and the orbital bone edge-aware (OBE) loss. First, intermediate slices are generated on the sagittal plane. Second, the generated intermediate slices are transformed to an axial image, which is then compared with the original axial image. To generate intermediate slices with an accurate orbital bone structure, the OBE loss considering the orbital bone structure on the sagittal and axial planes is used. To improve the perceptual quality of the generated intermediate slices, the feature map difference loss is additionally used on the axial plane. In the experiment, the proposed method showed the best performance among bilinear and bicubic interpolations, 3D SRGAN, and a 2D CNN-based method. Experimental results confirmed that the proposed method can generate intermediate slices with clear edges of thin bones as well as cortical bones on both the sagittal and the axial plane.