In flat-panel based cone beam computed tomography (CBCT), ring artifacts always exist and degrade the quality of reconstructed images. In this work, we propose a convolutional neural network (CNN) based ring artifact reduction algorithm in CT images, which fuses the information from the original and corrected images to eliminate the artifacts. The proposed method consists of two steps. First, we establish a database consisting of three types of images for training, artifact-free, ring artifact and pre-corrected images. Second, the original and pre-corrected images are input to the trained CNN to generate an image with less artifacts. To further reduce the artifacts, by using image mutual correlation, pixels in the pre-corrected image and the CNN output image, which are less sensitive to artifacts, are combined to generate a hybrid corrected image. Both simulated and real data experiments were performed to verify the proposed method. Experimental results show that the proposed method can effectively suppress the ring artifacts without introducing processing distortion to the image structure.
Dual energy cone beam computed tomography (DE-CBCT) can provide more accurate material characterization than conventional CT by taking advantages of two sets of projections with high and low energies. X-ray scatter leads to erroneous values of the DE-CBCT reconstructed images. Moreover, the reconstructed image of DECT is extremely sensitive to noise. Iterative reconstruction methods using regularization are capable to suppress the noise effects and hence improve the image quality. In this paper, we develop an algorithmic scatter correction based on physical model and statistical iterative reconstruction for DE-CBCT. With the assumption that the attenuation coefficients of the soft tissues are relatively stable and uniform and the scatter component is dominated by low frequency signal, scatter components were calculated while updating the reconstructed images in each iteration. Finally, the CBCT image was reconstructed by scatter corrected projections using statistical iterative reconstruction algorithm. Experiment shows that the proposed method can effectively remove the artifacts caused by x-ray scatter. The CT value accuracy in the reconstructed images has been improved.
Dual energy computed tomography (DECT) has significant impacts on material characterization, bone mineral density inspection, nondestructive evaluation and so on. In spite of great progress has been made recently on reconstruction algorithms for DECT, there still exist two main problems: 1) For polyenergetic X-ray source, the tube spectrum needed in reconstruction is not always available. 2) The reconstructed image of DECT is very sensitive to noise which demands special noise suppression strategy in reconstruction algorithm design. In this paper, we propose a novel method for DECT reconstruction that reconstructs tube spectrum from projection data and suppresses image noise by introducing ℓ1-norm based regularization into statistical reconstruction for polychromatic DECT. The contribution of this work is twofold. 1) A three parameters model is devised to represent spectrum of ployenergetic X-ray source. And the parameters can be estimated from projection data by solving an optimization problem. 2) With the estimated tube spectrum, we propose a computation framework of ℓ1-norm regularization based statistical iterative reconstruction for polychromatic DECT. Simulation experiments with two phantoms were conducted to evaluate the proposed method. Experimental results demonstrate the accuracy and robustness of the spectrum model in terms of that comparable reconstruction image quality can be achieved with the estimated and ideal spectrum, and validate that the proposed method works with attractive performance in terms of accuracy of reconstructed image. The root mean square error (RMSE) between the reconstructed image and the ground truth image are 7.648 × 10<sup>-4</sup> and 2.687 x 10<sup>-4</sup> for the two phantoms, respectively.