Properties Of Iterative Algorithms In SPECT Image Reconstruction

Abstract

We studied the properties of two iterative reconstruction algorithms, namely the ML-EM and the WLSCG algorithms, for use in compensation for attenuation and detector response in cardiac SPECT. A realistic phantom, derived from a patient X-ray CT image to simulate 201T1 SPECT data, was used in the study. Both algorithms are effective in compensating for the non-uniform attenuation distribution in the thorax region and the spatially variant detector response function of the imaging system. At low iteration numbers, additional compensation for detector response provides improvement in both spatial resolution and image noise when compared with attenuation compensation alone. However, at higher iteration numbers, there is a more rapid increase in image noise when detector response compensation is included, and the increase is more dramatic for WLS-CG algorithm. In general, the initial convergence rate of the WLS-CG algorithm is about ten times that of the ML-EM algorithm. Also, the WLS-CG exhibits a faster increase in image noise at large iteration numbers than the ML-EM algorithm. The investigation provides valuable information about the choice of iterative algorithms and optimum iteration number for improved cardiac SPECT image reconstruction.