Constrained least squares filtering in high resolution PET and SPECT imaging

Constrained least squares filtering is a technique which employs a priori tomograph response information for the filtering of projection data prior to the filtered backprojection of radionuclide distribution images. This paper describes a simulation study which evaluates the performance of this algorithm as a function of the sampling density within each projection of the radon transform. The results of this evaluation demonstrate that the efficient application of this algorithm requires higher sampling densities than are typically employed in high resolution PET and SPECT. Therefore, application of this algorithm requires software and/or hardware modification of the data acquisition sampling schemes employed in tomographic systems