IMAGE-RECONSTRUCTION FOR DYNAMIC PET BASED ON LOW-ORDER APPROXIMATIONAND RESTORATION OF THE SINOGRAM

Many image-reconstruction methods have been proposed to improve the sp atial resolution of positron emission tomography (PET)'images and, thu s, to produce better quantification, However, these techniques, which are designed for static images, may be inadequate for good reconstruct ion from dynamic data, We present a simple, but effective, reconstruct ion approach intended specifically for dynamic studies, First, the lev el of noise in dynamic PET data is reduced by smoothing along the time axis using a low-order approximation, Next, the denoised sinograms ar e restored spatially by the method of projections onto convex sets, Fi nally, images are reconstructed from the restored sinograms by ordinar y filtered backprojection, We present experimental results that demons trate substantial improvements in region-of-interest quantification in actual and simulated dopamine D-2 neuroreceptor-imaging studies of a monkey brain.