Using Bi-planar X-Ray Images to Reconstruct the Spine Structure by the Convolution Neural Network

The spine-related disease is one of the most common musculoskeletal-related disorder in the world. Although computed tomography (CT) is an outstanding tool for investigating spinal pathology in clinical protocol, the overexposure to radiation dose issue cannot be underestimated. Therefore, the bi-planar EOS X-ray imaging was adopted as the scanning technology, which can capture the anteroposterior (AP) and lateral (LAT) view X-ray images simultaneously with ultra-low radiation doses. High quality and high contrast bi-planar X-ray images would be acquired from the EOS system and these two radiographs enable a precise three-dimensional reconstruction of vertebrae, pelvis and other parts of the skeletal system. To overcome the time-consuming issue of spine reconstruction using the EOS system, a convolution neural network (CNN) was applied to reconstruct the entire spine model. Nowadays, the CNN model has already been adopted in the transformation from 2D image to 3D scenes. Our approach represents a potential alternative for EOS reconstruction while still maintaining a clinically acceptable diagnostic accuracy.