Fast Iterative Adaptive Reconstruction in Low-Dose CT Imaging

Traditional iterative tomographic reconstruction methods resort to gradient decent methods and require significant computation due to slow convergence. We divide the iterative reconstruction into two stages in the image and Radon spaces, respectively. First, we refine the reconstruction result by image space adaptive filtering. This finds a feasible update direction based on signal modeling. Second, we minimize the discrepancy between the sinograms along the update direction in Radon space and guarantee convergence. Reconstruction from clinical data using the proposed algorithm converges extremely fast and provides satisfactory reconstruction results in far fewer iterations than traditional methods.