PET IMAGE RECONSTRUCTION USING ANATOMICAL INFORMATION THROUGH MUTUAL INFORMATION BASED PRIORS: A SCALE SPACE APPROACH

摘要

We propose a mutual information based prior for incorporating information from co-registered anatomical images into PET image reconstruction. The prior uses mutual information between feature vectors that are extracted from the anatomical and functional images using a scale space approach. We perform simulations on a realistic 3D phantom generated by replicating a 2-D autoradiographic cross section of a mouse labeled with F18-FDG. A digital photograph of the cryosection of the same slice is used to generate the anatomical image. The images are registered using mutual information based rigid registration. PET data are then simulated from the autoradiography based phantom. We use a preconditioned conjugate gradient algorithm to compute the PET image that maximizes the posterior density. The performance of this method is compared with that using a Gaussian quadratic penalty, which does not use anatomical information. Simulation results indicate that the mutual information based prior can achieve reduced standard deviation at comparable bias compared to the quadratic penalty.