IMAGE RECONSTRUCTION IN EMISSION MEDICAL TOMOGRAPHY BY USING STATISTICAL APPROACH
The research directed on improvement of image quality in emission medical tomography is presented. The problem of image reconstruction with single-photon tomography (SPECT) and positron tomography (PET) is considered on the basis of a method of statistical modeling by using statistical algorithm MAP (Maximum a Posteriori) with iterative regularization. Numerical tests have been performed by using of the mathematical model, simulating a distribution of radiating isotopes in a brain (Hoffman phantom). Convergence properties of MAP algorithm are investigated in the conditions approached to clinical practice. Comparisons of MAP and OS-EM algorithms have been made. It is shown, that OS-EM algorithm does not converge to the stable solution and demands the visual control. MAP algorithm with iterative regularization allows one to receive steady convergence to the 'probable' decision.