Comparison Between ML-EM And WLS-CG Algorithms For Spect Image Reconstruction

Abstract

We have studied the properties of two iterative reconstruction algorithms, namely, the maximum likelihood with expectation maximization (ML-EM) and the weighted least squares with conjugate gradient (WLS-CG) algorithms, for use in compensation for attenuation and detector response in cardiac SPECT imaging. A realistic phantom, derived from a patient X-ray CT study to simulate ’OITP SPECT data, was used in the investigation. Both algorithms are effective in compensating for the nonuniform attenuation distribution in the thorax region and the spatially variant detector response function of the imaging system. At low iteration numbers, the addition of detector response compensation 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 the WLS-CG algorithm. In general, the 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. This study is valuable in the search for useful and practical reconstruction methods for improved clinical cardiac SPECT imaging.