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.

A New General Maximum Intensity Projection Technology via the Hybrid of U-Net and Radial Basis Function Neural Network

“…With the maturity of technology, RBFNN has received considerable attention from researchers in various fields due to its simple structure, strong nonlinear approximation ability, and good generalization ability. It is widely used in many research fields, including pattern classification, function approximation, and data mining [23][24][25]. In RBFNN, the Gaussian function is the most commonly used radial basis function to effectively activate the logical relationship between the input layer and the hidden layer [24].…”

“…In turn, the entire projected image could be obtained. In addition, according to our previous results [25,26], in medical image processing, one of the key aspects necessary to improve the intelligence of RBFNN is to effectively select or calculate the feature points of the pending images which are the neurons that make up the input layer of the neural network.…”

“…We set the learning rate of shallow network and iteration number range to 0.01 and 200-300, respectively. The loss function of RBFNN was also selected based on classic MSE [25]. More importantly, the original 2D image used to synthesize the MIP image was used as the input of the initial neural network.…”

3D ring artifacts removal algorithm combined low‐rank tensor decomposition with spatial–sequential total variation regularization and its application in phase‐contrast microtomography

“…Although this convolutional neural network method obtains good results, the large amount of required training data (1 × 10 5 samples of CT image data) consumes considerable amounts of time. Moreover, in the phase contrast imaging field, data are scarce 15,16,17 …”

“…Moreover, in the phase contrast imaging field, data are scarce. 15,16,17 All the above algorithms applied traditional 2D (matrixbased) methods to remove the ring artifacts in CT images one by one.However,this kind of method ignores the high correlations hidden in sequential CT images, and some important priors are neglected. Thus, how to make full use of this characteristic to improve the ring artifacts removal performance is a noteworthy problem.…”