Kinetic modelling in positron emission tomography (PET) has evolved alongside scanner development over the past 50 years. Yet, despite its central role in quantitative analysis, a single, comprehensive resource covering both theory and applications has been missing. Building on decades of expertise at Turku PET Centre, Modeller’s Guide to PET Imaging brings this knowledge together in a clear, systematic textbook. 

Designed for clinicians, physicists, and researchers alike, this book offers a structured introduction to the analysis of dynamic PET data. It combines theoretical foundations with practical implementation, explaining mathematical concepts, statistical methods, and modelling techniques through accessible text, illustrations, and fully worked Python examples. 

The book spans the full workflow of PET analysis—from imaging physics and data processing to advanced kinetic modelling. Core topics include compartment models, graphical approaches such as Patlak and Logan plots, and spectral analysis, alongside applications across major organs. 

With more than 120 exercises and extensive code examples, readers are encouraged to apply concepts directly to real PET data, making this both a practical learning resource and a long-term reference.

Riku Klén has completed his MSc and PhD in Mathematics at the University of Turku, Finland, and is currently working as an associate professor of Imaging Instrumentation and Detection Technologies at Turku PET Centre in Finland. Until 2016, his research focus was on mathematical analysis and especially in Geometric Function Theory, and from 2016 to 2019, he concentrated in the development of machine learning and medical data analysis methods. From 2019 onward, Klén has focused on positron emission tomography related research including imaging instrumentation and image analysis, including automated methods utilising machine learning. Besides his academic career, Klén has worked as an associate member of the Finnish matriculation examination board at Finnish Ministry of Education since 2011, and from 2020 onward as the chief scientific officer in company Ai2Ai Ltd. Klén has co-authored two scientific textbooks Harjulehto-Klén-Koskenoja: Analyysiä reaaliluvuilla, which is used in Finnish universities for the first year Mathematical Analysis, and Hariri-Klén-Vuorinen: Conformally Invariant Metrics and Quasiconformal Mappings.

Oona Rainio completed an MSc degree in Cryptography and Data Security, another MSc in Statistics, and a PhD in Mathematics at the University of Turku, Finland. After finishing her PhD in 2021, she started working as a postdoctoral researcher in the group Into Imaging at Turku PET Centre in Finland. Her current work is interdisciplinary research utilising different methods of deep learning, scientific computing, and statistics to interpret results of clinical imaging studies. She has published scientific articles on various topics including hyperbolic geometry, statistics, medical imaging, kinetic modelling, and convolutional neural networks. In 2025, she was awarded the title of docent by the department of mathematics and statistics of the University of Turku.