SPECT image reconstruction using compound models
SPECT (single photon emission computed tomography) is used in nuclear medicine to determine the distribution of a radioactive isotope within a patient from tomographic views or projection data. These images are severely degraded due to the presence of noise and several physical factors like attenuation and scattering. We use, within the Bayesian framework, a compound Gauss Markov random field (CGMRF) as prior model to reconstruct such images. In order to find the maximum a posteriori (MAP) estimate we propose a new iterative method, which is stochastic for the line process and deterministic for the reconstruction. The proposed method is tested and compared with other reconstruction methods on both synthetic and real SPECT images.