Correction of geometric artifact in cone-beam computed tomography through a deep neural network.

Cone-beam computed tomography is a noninvasive detection system that can obtain the three-dimensional structure of objects in a way that does not damage the object. It is widely applied in precision instruments, medical detection, and other fields. However, in the actual process, if a geometric artifact appears in the results, it will affect the quality of reconstructed images, including detail loss and decreased spatial resolution, which leads to inaccurate distinction of defects in detection. We propose a method for correcting a geometric artifact by means of data-driven projection and neural networks. The network designed is a deep neural network with six convolutional layers and six deconvolutional layers that can correct a geometric artifact by training a large number of labeled data and unlabeled data. Compared with other networks that require prior information for reconstructed images, the proposed method uses a projection data-driven approach that can avoid the requirement for prior information. The simulation data have been tested under varying degrees of noise, and satisfactory geometric artifact correction results have been obtained. Meanwhile, we use the actual data of line pairs and ball grid array solder joints to conduct experiments. The results obtained by our method are compared with two other phantom-based method and the U-net method, respectively. The results of similarity and spatial resolution show that the proposed method can achieve the comparable results as the two types of methods. At the same time, we apply a projection data-driven approach to avoid the requirement for prior information, which is more conducive to the correction of the geometric artifact in practical situations where prior information is lacking.