Pharmacokinetic Modelling for Dynamic Tumor Characterization using DCE-MRI

Pharmacokinetic Modelling for Dynamic Tumor Characterization using DCE-MRI presents a comprehensive mathematical and computational framework for analyzing Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) data for quantitative tumor characterization. The book introduces the fundamentals of MRI, contrast agents, and DCE-MRI before progressing into pharmacokinetic and compartmental modelling, mathematical foundations, and conventional models such as the Tofts, Extended Tofts, and Brix models. It provides detailed treatment of two-compartment and three-compartment models, including their governing differential equations, state-space representations, numerical solutions, and physiological interpretations. The book also covers DCE-MRI data processing, voxel-wise signal and concentration analysis, parameter estimation using nonlinear least-squares methods and Levenberg-Marquardt optimization, and Particle Swarm Optimization (PSO). Particular attention is given to spatial parameter modelling, tumor heterogeneity, spatial regularization, parameter map generation, and computational simulation using MATLAB. The later chapters explore applications in tumor perfusion, vascularity, tissue characterization, treatment response assessment, therapy monitoring, and personalized medicine, followed by experimental and comparative analysis using RMSE, R², AIC, BIC, parameter stability, and computational performance. Designed for students, researchers, engineers, medical imaging professionals, and biomedical researchers, this book provides a structured reference for understanding how mathematical modelling and optimization can transform dynamic MRI data into meaningful quantitative information for tumor characterization and analysis.