3-D weighted CBCT iterative reconstruction with TV minimization from angularly under-sampled projection data

With increasing cone angle in cone beam computed tomography (CBCT), the CB artifacts due to cone angle in reconstructed images deteriorate, mainly because of increased severity of violating the data sufficiency condition (DSC). The CB artifacts in analytic reconstruction can be partially attributed to the data inconsistency between the conjugate rays that are 180° apart in their view angles; while those in iterative reconstruction result from the hypothesis that the error propagates preferentially along the rays with larger cone angles. The generation of CB artifacts in both sorts of reconstructions is closely related to the cone angle. In this paper, we analyze how the errors in the CB reconstructions are induced and propagated; meanwhile, inspired by the success of and in a way similar to the three-dimensional (3-D) weighted CB-FBP algorithm, we incorporate a similar 3-D weighting into the total-variation (TV) minimization based CB iterative algorithm for reconstructing images from angularly under-sampled projection data and suppressing the CB artifacts in the reconstructed images. Numerical simulation studies with the FORBILD thoracic phantom have been conducted. In the 3-D weighted CB iterative reconstruction, the CB artifacts can be effectively reduced, although there is an obvious distinction in the appearances of the CB artifacts in analytic and iterative reconstructions. In this work, we have developed a 3-D weighted CB iterative reconstruction with TV minimization, which does suppress the CB artifacts and improve the quality of the reconstructed images.