A NONLINEAR, IMAGE DOMAIN FILTERING METHOD FOR CARDIAC PET IMAGES

An adaptive, nonlinear image domain filtering strategy is described wh ich improves positron emission tomography (PET) images. The method was formulated to improve on the linear, low-pass filtering typically app lied to each projection in the filtered back-projection (FBP) reconstr uction algorithm. The algorithm is a potential alternative to linear s moothing which reduces noise but degrades resolution; this method uses the FBP algorithm for reconstruction, but aims to incorporate some of the statistical information and nonlinear smoothing utilized in itera tive reconstruction algorithms. The approach uses sinogram segmentatio n to separate the sinogram elements with higher and lower signal-to-no ise ratios, and then reconstruct each with FBP using a more appropriat e choice of filter and cut-off frequency. Also, this algorithm address es the radial streak artifacts introduced by FBP. The algorithm was ev aluated using simulations and clinical data of cardiac PET studies on an ECAT 931 PET scanner. The initial results suggest that this techniq ue has advantages over the current clinical protocol. Images processed with the method show generally improved visual image quality and redu ced radial streaks without the introduction of artifacts. In simulatio ns, increased contrast recovery and resolution are realized without an increase in the background noise of the reconstructed images.