A computer-implemented method for training a deep convolutional neural network to reduce one or more artifacts in at least one projection space image, the method comprising: collecting a plurality of view-adjacent, artifact contaminated cone beam computed tomography projection space images, and a corresponding artifact reduced, predetermined one of the plurality of view-adjacent cone beam computed tomography projection space images from each patient in a group of patients; and using the plurality of view-adjacent, artifact contaminated cone beam computed tomography projection space images and the corresponding artifact reduced, predetermined one of the plurality of view-adjacent cone beam computed tomography projection space images collected from each patient in the group of patients to train a deep convolutional neural network to reduce one or more artifacts in at least a predetermined one of a plurality of view-adjacent, cone beam computed tomography projection space images; wherein training the deep convolutional neural network comprises, for each plurality of view-adjacent, artifact contaminated cone beam computed tomography projection space images: applying the deep convolutional neural network to provide an estimated artifact-reduced result; and updating parameters of the deep convolutional neural network based on a comparison of the estimated artifact-reduced result with the corresponding artifact-reduced, predetermined one of the plurality of view-adjacent cone beam computed tomography projection space images. collecting a plurality of view-adjacent, artifact contaminated cone beam computed tomography projection space images, and a corresponding artifact reduced, predetermined one of the plurality of view-adjacent cone beam computed tomography projection space images from each patient in a group of patients; and using the plurality of view-adjacent, artifact contaminated cone beam computed tomography projection space images and the corresponding artifact reduced, predetermined one of the plurality of view-adjacent cone beam computed tomography projection space images collected from each patient in the group of patients to train a deep convolutional neural network to reduce one or more artifacts in at least a predetermined one of a plurality of view-adjacent, cone beam computed tomography projection space images; wherein training the deep convolutional neural network comprises, for each plurality of view-adjacent, artifact contaminated cone beam computed tomography projection space images: applying the deep convolutional neural network to provide an estimated artifact-reduced result; and updating parameters of the deep convolutional neural network based on a comparison of the estimated artifact-reduced result with the corresponding artifact-reduced, predetermined one of the plurality of view-adjacent cone beam computed tomography projection space images. applying the deep convolutional neural network to provide an estimated artifact-reduced result; and updating parameters of the deep convolutional neural network based on a comparison of the estimated artifact-reduced result with the corresponding artifact-reduced, predetermined one of the plurality of view-adjacent cone beam computed tomography projection space images.