Accurate estimation of the Fisher information matrix for the PET image reconstruction problem.
The Fisher information matrix (FIM) plays a key role in the analysis and applications of statistical image reconstruction methods based on Poisson data models. The elements of the FIM are a function of the reciprocal of the mean values of sinogram elements. Conventional plug-in FIM estimation methods do not work well at low counts, where the FIM estimate is highly sensitive to the reciprocal mean estimates at individual detector pairs. A generalized error look-up table (GELT) method is developed to estimate the reciprocal of the mean of the sinogram data. This approach is also extended to randoms precorrected data. Based on these techniques, an accurate FIM estimate is obtained for both Poisson and randoms precorrected data. As an application, the new GELT method is used to improve resolution uniformity and achieve near-uniform image resolution in low count situations.
Li
Quanzheng
Q
Signal and Image Processing Institute, Univ of Southern California, Los Angeles, CA 90089, USA.
Asma
Evren
E
Qi
Jinyi
J
Bading
James R
JR
Leahy
Richard M
RM
D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014055:Tomography, Emission-Computed / Q000295:instrumentation / Q000379:methods*