Four-Dimensional CBCT Reconstruction Based on a Residual Convolutional Neural Network for Improving Image Quality

In radiation treatment, a cone-beam computed tomography (CBCT) scan is conducted for precise positioning of tumors, and the image quality is usually degraded by motion artifacts due to patient’s respiration and movement during scanning. Four-dimensional (4D) CBCT reconstruction with phase binning is typically used to overcome these difficulties. Albeit motion artifacts might be reduced with 4D CBCT, the overall image quality is typically worsened by severe streak artifacts due to the sparse-angle projections available in the 3D reconstruction for each motion phase. This study presents a method for reducing streak artifacts effectively in conventional 4D CBCT reconstruction by using a state-of-the-art convolutional neural network (a residual U-Net was used). We performed a computational simulation and an experiment to investigate the image quality and evaluate the effectiveness of the proposed method. The proposed 4D CBCT reconstruction method reduced streak artifacts noticeably, and its effectiveness was validated by comparing its results to those of other reconstruction methods such as the filtered-backprojection, a compressed-sensing, and a simple CNN-based algorithm for the 4D CBCT datasets.