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 (FAIR-B). 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 expectation maximisation-maximum likelihood (EM-ML). However, for low-contrast regions whilst improved structural accuracy is still obtained, contrast losses are observed.