Handbook of Tractography presents methods and applications of MR diffusion tractography, providing deep insights into the theory and implementation of existing tractography techniques and offering practical advice on how to apply diffusion tractography to research projects and clinical applications. Starting from the design of MR acquisition protocols optimized for tractography, the book follows a pipeline approach to explain the main methods behind diffusion modelling and tractography, including advanced analysis of tractography data and connectomics. An extensive section of the book is devoted to the description of tractography applications in research and clinical settings to give a complete picture of tractography practice today.
By focusing on technology, models and applications, this handbook will be an indispensable reference for researchers and students with backgrounds in computer science, mathematics, physics, neuroscience and medical science.
1. 1 Introduction - p. 275 2. 2 The Riemann-DTI paradigm - p. 276 3. 3 Conclusion - p. 289 4. Appendix: Basic concepts from linear algebra and tensor calculus - p. 289 5. References - p. 292 6. Chapter 16 Global tractography - p. 297 7. Chapter 17 Machine learning in tractography - p. 315 8. Chapter 18 Improving tractography using anatomical priors and multimodal integration - p. 347 9. Chapter 19 Tractography in pathological anatomy: Some general considerations - p. 363 10. Chapter 20 Tractography visualization - p. 381 11. Part IV: From streamlines to tracts - p. 393 12. Chapter 21 Dissecting white matter pathways: A neuroanatomical approach - p. 397 13. Chapter 22 Dissecting white matter pathways: Automatic and semiautomatic approaches - p. 423 14. Chapter 23 Methods and statistics for diffusion MRI tractometry - p. 439 15. Chapter 24 Connectivity and connectomics - p. 451 16. Chapter 25 Tractography validation Part 1: Foundations, numerical simulations, and phantom models - p. 485 17. Chapter 26 Tractography validation Part 2: The use of anatomical model systems and measures for validation - p. 511 18. Chapter 27 Tractography validation part 3: Lessons learned through validation studies - p. 543 19. Chapter 28 Current challenges and opportunities for tractography - p. 565 20. Part V: Tractography applications - p. 580 21. Chapter 29 Tractography: Applications to neurodevelopment, aging, and plasticity - p. 583 22. Chapter 30 Linking behavior with white matter networks - p. 613 23. Chapter 31 Neurosurgical applications of clinical tractography - p. 631 24. Chapter 32 Preclinical and ex vivo tractography: Techniques and applications at high field - p. 653 25. Chapter 33 Multicenter studies and harmonization: Problems, solutions, and open challenges - p. 669 26. Appendix A Vectors and tensors - p. 691 27. Appendix B Numerical integration - p. 697 28. Appendix C Interpolation, splines, and smoothing - p. 705 29. Appendix D Spherical harmonics - p. 711 30. Index - p. 717
Handbook of Tractography presents methods and applications of MR diffusion tractography, providing deep insights into the theory and implementation of existing tractography techniques and offering practical advice on how to apply diffusion tractography to research projects and clinical applications. Starting from the design of MR acquisition protocols optimized for tractography, the book follows a pipeline approach to explain the main methods behind diffusion modelling and tractography, including advanced analysis of tractography data and connectomics. An extensive section of the book is devoted to the description of tractography applications in research and clinical settings to give a complete picture of tractography practice today.
By focusing on technology, models and applications, this handbook will be an indispensable reference for researchers and students with backgrounds in computer science, mathematics, physics, neuroscience and medical science.
1. 1 Introduction - p. 275 2. 2 The Riemann-DTI paradigm - p. 276 3. 3 Conclusion - p. 289 4. Appendix: Basic concepts from linear algebra and tensor calculus - p. 289 5. References - p. 292 6. Chapter 16 Global tractography - p. 297 7. Chapter 17 Machine learning in tractography - p. 315 8. Chapter 18 Improving tractography using anatomical priors and multimodal integration - p. 347 9. Chapter 19 Tractography in pathological anatomy: Some general considerations - p. 363 10. Chapter 20 Tractography visualization - p. 381 11. Part IV: From streamlines to tracts - p. 393 12. Chapter 21 Dissecting white matter pathways: A neuroanatomical approach - p. 397 13. Chapter 22 Dissecting white matter pathways: Automatic and semiautomatic approaches - p. 423 14. Chapter 23 Methods and statistics for diffusion MRI tractometry - p. 439 15. Chapter 24 Connectivity and connectomics - p. 451 16. Chapter 25 Tractography validation Part 1: Foundations, numerical simulations, and phantom models - p. 485 17. Chapter 26 Tractography validation Part 2: The use of anatomical model systems and measures for validation - p. 511 18. Chapter 27 Tractography validation part 3: Lessons learned through validation studies - p. 543 19. Chapter 28 Current challenges and opportunities for tractography - p. 565 20. Part V: Tractography applications - p. 580 21. Chapter 29 Tractography: Applications to neurodevelopment, aging, and plasticity - p. 583 22. Chapter 30 Linking behavior with white matter networks - p. 613 23. Chapter 31 Neurosurgical applications of clinical tractography - p. 631 24. Chapter 32 Preclinical and ex vivo tractography: Techniques and applications at high field - p. 653 25. Chapter 33 Multicenter studies and harmonization: Problems, solutions, and open challenges - p. 669 26. Appendix A Vectors and tensors - p. 691 27. Appendix B Numerical integration - p. 697 28. Appendix C Interpolation, splines, and smoothing - p. 705 29. Appendix D Spherical harmonics - p. 711 30. Index - p. 717
