Image Reconstruction Using OS-EM Method in Cone-beam CT

Abstract

Traditional CT reconstructions are limited by many kinds of artifacts, and they give dissatisfactory image. To reduce image noise and artifacts, we propose a statistical iterative method for image reconstruction in cone-beam CT. First the theory of maximum likelihood estimation (MLE) is extended from emission CT to X-ray scan, then an expectation-maximization (EM) formula is deduced for direct reconstruction of cone-beam CT. EM algorithm is an iterative method that can produce good quality reconstruction, but compared with fast and robust FDK algorithm, EM algorithm is computer intensive and convergence slow. In order to accelerate the convergence speed of EM algorithm, ordered subset (OS) is applied in Cone-beam CT. Experimental results with computer simulated data and real CT data show that OS-EM algorithm can provide good quality reconstructions after only a few iterations. In addition, the point spread function of the OS-EM algorithm is analyzed for evaluating the imaging system performance.