Statistical Computing in Nuclear Imaging


Download Statistical Computing in Nuclear Imaging written by Arkadiusz Sitek in PDF format. This book is under the category Medicine and bearing the isbn/isbn13 number 143984934X/9781439849347. You may reffer the table below for additional details of the book.

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The ebook Statistical Computing in Nuclear Imaging (PDF) introduces aspects of Bayesian computing in nuclear imaging. The textbook provides an introduction to Bayesian statistics and concepts and is highly focused on the computational aspects of Bayesian data analysis of photon-limited data acquired in tomographic measurements. Basic statistical concepts; elements of decision theory; and counting statistics; including models of photon-limited data and Poisson approximations; are discussed in the first chapters. Monte Carlo methods and Markov chains in the posterior analysis are discussed next along with an introduction to nuclear imaging and applications such as SPECT and PET.

The final chapter includes illustrative examples of statistical computing; based on Poisson-multinomial statistics. Examples include calculation of Bayes factors and risks as well as Bayesian decision making and hypothesis testing. Appendices cover probability distributions; elements of set theory; multinomial distribution of single-voxel imaging; and derivations of sampling distribution ratios. C++ code used in the final chapter is also provided.

The ebook can be used as a textbook that provides an introduction to Bayesian statistics and advanced computing in medical imaging for mathematicians; physicists; engineers; and computer scientists. It is also a valuable resource for a wide spectrum of practitioners of nuclear imaging data analysis; including seasoned scientists and researchers who have not been exposed to Bayesian paradigms.

Additional information


Arkadiusz Sitek


CRC Press




275 pages




B07L6V5W1F; B00S9OM0XY





Table of contents

Table of contents :
Content: Basic Statistical Concepts Introduction Before- and After-the-Experiment Concepts Definition of Probability Joint and Conditional Probabilities Statistical Model Likelihood Pre-Posterior and Posterior Extension to Multi-Dimensions Unconditional and Conditional Independence Summary Elements of Decision Theory Introduction Loss Function and Expected Loss After-the-Experiment Decision Making Before-the-Experiment Decision Making Robustness of the Analysis Counting Statistics Introduction to Statistical Models Fundamental Statistical Law Exact Models of Photon-Limited Data Poisson Approximation Normal Distribution Approximation Monte Carlo Methods in Posterior Analysis Monte Carlo Approximations of Distributions Monte Carlo Integrations Monte Carlo Summations Markov Chains Basics of Nuclear Imaging Nuclear Radiation Radiation Detection in Nuclear Imaging Nuclear Imaging Dynamic Imaging and Kinetic Modeling Applications of Nuclear Imaging Statistical Computing Computing Using Poisson-Multinomial Statistics Examples of Statistical Computing Appendix A Probability Distributions Appendix B Elements of Set Theory Appendix C Multinomial Distribution of Single-Voxel Imaging Appendix D Derivations of Sampling Distribution Ratios Appendix E Equation (6.11) Appendix F C++ Code of the OE Algorithm for STS References

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