Statistical Computing in Nuclear Imaging

This book is highly focused on computational aspects of Bayesian data analysis of photon-limited data acquired in tomographic measurements in nuclear imaging. Basic Bayesian statistical concepts, elements of Bayesian decision theory, and counting statistics are discussed in the first chapters. Monte Carlo methods and Markov chains in posterior analysis are discussed next along with an introduction to nuclear imaging and applications such as PET and SPECT. The final chapter includes illustrative examples of statistical computing based on Poisson-multinomial statistics. Examples include calculation of Bayes factors and risks, and Bayesian decision making and hypothesis testing. C++ code used in the final chapter is also provided.