An Empirical Data Inconsistency Metric (DIM) Driven CT Image Reconstruction Method

Current computed tomography (CT) image reconstruction methods generally assume that a complete and consistent data set was acquired during data acquisition. In practice, however, the acquired data are often not consistent from one view angle to another. In this case, the application of a well-developed image reconstruction algorithm to an inconsistent data set still generates artifacts in the reconstructed images. Therefore, it is highly desirable to develop a simple method to classify the acquired data set into several consistency classes and to incorporate the data consistency information into an image reconstruction framework to simultaneously reconstruct several sub-images of the same image object according to the data consistency level of the acquired data set. In this paper, an empirical data inconsistency metric (DIM) was introduced to characterize the inconsistency level of the acquired cone beam CT projection data at each view angle caused by the polychromatic X-ray spectrum and the use of tube potential modulation. The entire acquired data set is then sorted into several subsets of view angles based upon the value of the DIM at each view angle. After data classification, the previously published algorithm, synchronized multiartifact reduction with tomographic reconstruction, was applied to simultaneously reconstruct images for these sub-images in different spectral consistency classes. Each sub-image is consistent with the subset of the projection view angles for a given range of DIM values. To validate the method, numerical simulation studies from an anthropomorphic numerical phantom, a hybrid phantom of known truth with human anatomy, and in vivo human subject data were conducted to demonstrate the practical utility of the proposed method.