Enhanced parameter estimation with GLLS and the Bootstrap Monte Carlo method for dynamic SPECT

The generalized linear least squares (GLLS) method has been shown to successfully construct unbiased parametric images from dynamic positron emission tomography (PET). However, the high level of noise intrinsic in single photon emission computed tomography (SPECT) can give rise to unsuccessful voxel-wise fitting using GLLS, resulting in physiologically meaningless estimates, such as negative kinetic parameters for compartment models. In this study, three approaches were investigated to improve the reliability of GLLS applied to dynamic SPECT data. The simulation and experimental results showed that GLLS with the aid of Bootstrap Monte Carlo method proved successful in generating parametric images and preserving all of the major advantages of all the originally GLLS method, although at the expense of increased computation time