Study of the effects of using anatomical priors in PET reconstruction

Presents preliminary results of a Monte-Carlo study in which the authors quantify the relative performance of several PET image reconstruction procedures. Through the use of ensemble averages they estimate bias and variance on a pixel-by-pixel basis and also for larger regions of interest (ROIs). They compare the performance of filtered back-projection (FBP), maximum likelihood (ML) and several maximum a posteriori (MAP) estimators. The MAP estimators are based on Markov random field image models with the following Gibbs energy functions: (i) quadratic, (ii) Geman and McClure (1985), (iii) weak membrane model, (iv) weak membrane with additional line process interactions. In all cases the MAP estimators use an automated procedure to chose the prior parameter which is based on the “L-curve” as described below. The weak membrane prior includes a line process which models the locations of discontinuities in image intensity. In cases where prior information, in the form of an anatomical MR image, is available concerning the potential locations of discontinuities in the PET image, this can be incorporated into the prior and used to influence the formation of boundaries. The impact of this additional information on the reconstructed image is quantified through the authors´ Monte-Carlo study. Results indicate that MAP estimation procedures are able to reduce variance considerably, in comparison to ML, with a corresponding small increase in bias. The addition of boundary information results in further reductions in variance and reduced bias