Fast accurate iterative three-dimensional Bayesian reconstruction for low-statistics positron volume imaging

摘要

Direct use of list-mode data for image reconstruction improves accuracy for some imaging systems, and permits fast reconstructions for low-statistics situations. A list-mode based three-dimensional implementation of an iterative Bayesian reconstruction algorithm has been developed. The approach starts with an initial 2-D filtered backprojection (FBP) of Fourier rebinned data and employs a Gibbs prior to encourage images with local continuity, using the method of iterative conditional averages to obtain a sequence of estimates. Ten iterations are sufficient to significantly affect the image, incorporating the benefits of list-mode data and the Gibbs prior. The method has been tested with simulated data for rotating planar detector based systems and can offer improved noise-contrast behaviour over FBP and list-mode driven EM-ML. However, for low-contrast regions whilst improved structural accuracy is still obtained, contrast losses are observed.