Removal of computed tomography ring artifacts via radial basis function artificial neural networks

Abstract

Ring artifacts in computed tomography (CT) images are caused by the undesirable response of detector pixels, which leads to the degradation of CT images. Accordingly, it affects the image interpretation, post-processing, and quantitative analysis. In this study, a radial basis function neural network (RBFNN) was used to remove ring artifacts. The proposed method employs polar coordinate transformation. First, ring artifacts were transformed into linear artifacts by polar coordinate transformation. Then, smoothing operators were applied to locate these artifacts exactly. Subsequently, RBFNN was operated on each linear artifact. The neuron numbers of the input, hidden, and output layers of the neural network were 8, 40, and 1, respectively. Neurons in the input layer were selected according to the characteristics of the artifact itself and its relationship with the surrounding normal pixels. For the training of the neural network, a hybrid of adaptive gradient descent algorithm (AGDA) and gravitational search algorithm (GSA) was adopted. After the corrected image was obtained using the updated neural network, the inverse coordinate transformation was implemented. The experimental data were divided into simulated ring artifacts and real ring artifacts, which were based on brain and abdomen CT images. Compared with current artifact removal methods, the proposed method removed ring artifacts more effectively and retained the maximum detail of normal tissues. In addition, for index analysis, the performance of proposed method was superior to that of the other methods.