Improve 3D cone-beam CT reconstruction by slice-wise deep learning

X-ray cone beam CT (CBCT) is widely used in many fields such as clinic diagnosis, public safety inspection, and nondestructive testing. In recent years, radiation risk gets growing concern by the public. As a result, X-ray cone-beam CT (CBCT) imaging of low dose is becoming more and more helpful in many fields especially clinic diagnosis and public safety inspection. To get better reconstruction results, a typical idea is to add useful and meaningful constraints in reconstruction procedure. The evaluation criterion of reconstruction could be a general target function concerning both physical models and specific constraints. As computer hard-ware evolves greatly, optimization-based iterative algorithms to minimizing these functions have been proposed and proved helpful for low-dose case. However, these methods are usually time consuming. Nowadays, deep learning method has been utilized in image processing. In CT fields, it has been shown that reconstruction algorithms based on convolutional neural network (CNN) is effective in solving problems with low-dose or sparse-view case. In our work, we proposed a novel CNN structure which is designed for transferring cone-beam projection to fan-beam projection of each cross section attenuation map of an object with noise reduction. After the 3D problem is turned to be a 2D one, we simply use the system matrix to reconstruct the object. Because of its slice-wise character, the method can effectively simplify the 3D reconstruction problem.