Bayesian Reconstruction Using a New Nonlocal MRF Prior for Count-Limited PET Transmission Scans

摘要

Transmission scans are performed to provide attenuation correction factors (ACFs) information for positron emission tomography (PET). Long acquisition or scan times for transmission tomography, although alleviating the noise effect of the count-limited scans, are blamed for patient uncomfortableness and movements. So, the quality of transmission tomography from short scan time often suffers heavily from noise effect and limited counts. Bayesian approaches, or maximum a posteriori (MAP) methods, have been accepted as an effective solution to overcome the ill-posed problem of count-limited transmission tomography. Based on Bayesian and Markov Random Fields(MRF)theories, prior information of the objective image can be incorporated to improve the reconstructions from count-limited and noise-contaminating transmission scans. However, information of traditional priors comes from a simply weighted differences between the pixel densities within local neighborhoods, so only limited prior information can be provided for reconstructions. In this paper, a novel nonlocal MRF prior, which is able to exploit global information of image by choosing large neighborhoods and a new weighting method, is proposed.Two-step monotonical reconstruction algorithm is also given for PET transmission tomography. Experimentations show that the reconstructions using the nonlocal prior can reconstruct better transmission images and overcome the ill-posed problem even when the scan time is relatively short.