A nonlinear, image domain filtering method for PET images

An adaptive, nonlinear image domain filtering strategy is described which improves positron emission tomography (PET) images. The method was formulated to improve on the linear, low-pass filtering typically applied to each projection in the filtered back-projection (FBP) reconstruction algorithm. The algorithm is a potential alternative to linear smoothing 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 iterative reconstruction algorithms. The approach uses sinogram segmentation to separate the sinogram elements with higher and lower signal-to-noise ratios, and then reconstruct each with FBP using a more appropriate choice of filter and cut-off frequency. Also, this algorithm addresses the radial streak artifacts introduced by FBP. The algorithm was evaluated using simulations as well as clinical data of cardiac PET studies on an ECAT 931 PET scanner. The initial results suggest that this technique has advantages over the current clinical protocol. Images processed with the method show generally improved visual image quality and reduced radial streaks without the introduction of artifacts. Also, by using a higher resolution filter for the high activity segment of the sinogram, increased contrast recovery and resolution are realized