  X-ray images ease the diagnosis and treatment process due to their rapid
imaging speed and high resolution. However, due to the projection process of
X-ray imaging, much spatial information has been lost. To accurately provide
efficient spinal morphological and structural information, reconstructing the
3-D structures of the spine from the 2-D X-ray images is essential. It is
challenging for current reconstruction methods to preserve the edge information
and local shapes of the asymmetrical vertebrae structures. In this study, we
propose a new Edge-Aware Reconstruction network (EAR) to focus on the
performance improvement of the edge information and vertebrae shapes. In our
network, by using the auto-encoder architecture as the backbone, the edge
attention module and frequency enhancement module are proposed to strengthen
the perception of the edge reconstruction. Meanwhile, we also combine four loss
terms, including reconstruction loss, edge loss, frequency loss and projection
loss. The proposed method is evaluated using three publicly accessible datasets
and compared with four state-of-the-art models. The proposed method is superior
to other methods and achieves 25.32%, 15.32%, 86.44%, 80.13%, 23.7612 and
0.3014 with regard to MSE, MAE, Dice, SSIM, PSNR and frequency distance. Due to
the end-to-end and accurate reconstruction process, EAR can provide sufficient
3-D spatial information and precise preoperative surgical planning guidance.
