title: Regularization parameter selection for penalized-likelihood list-mode image reconstruction in PET.
abstract: Penalized likelihood (PL) reconstruction has demonstrated potential to improve image quality of positron emission tomography (PET) over unregularized ordered-subsets expectation-maximization (OSEM) algorithm. However, selecting proper regularization parameters in PL reconstruction has been challenging due to the lack of ground truth and variation of penalty functions. Here we present a method to choose regularization parameters using a cross-validation log-likelihood (CVLL) function. This new method does not require any knowledge of the true image and is directly applicable to list-mode PET data. We performed statistical analysis of the mean and variance of the CVLL. The results show that the CVLL provides an unbiased estimate of the log-likelihood function calculated using the noise free data. The predicted variance can be used to verify the statistical significance of the difference between CVLL values. The proposed method was validated using simulation studies and also applied to real patient data. The reconstructed images using optimum parameters selected by the proposed method show good image quality visually.
authors.affiliation: Department of Biomedical Engineering, University of California, Davis, CA, United States of America.
authors.forename: Mengxi
authors.initials: M
authors.lastname: Zhang
authors.forename: Jian
authors.initials: J
authors.lastname: Zhou
authors.forename: Xiaofeng
authors.initials: X
authors.lastname: Niu
authors.forename: Evren
authors.initials: E
authors.lastname: Asma
authors.forename: Wenli
authors.initials: W
authors.lastname: Wang
authors.forename: Jinyi
authors.initials: J
authors.lastname: Qi
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016013:Likelihood Functions; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography