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Under-sampling and insufficient data result in a big challenge in the reconstruction of x-ray computed tomographic (CT) images. In addition, patient's respiratory motion also deteriorates this reconstruction process as it normally leads to blurred outputs. In this work, we propose an iterative method with a combination of total variation (TV) regularization and principle component analysis (PCA) regularization. Partial prior knowledge of the CT images, obtained through PCA analysis of training images is incorporated in the reconstruction process. Numerical experiments are performed in the context of a fan-beam CT reconstruction, which shows advantages of our method over the ones with just TV regularization or just PCA regularization.