Handbook of Diffusion MR Tractography

Imaging Methods, Biophysical Models, Algorithms and Applications

1st Edition - November 19, 2024
Editors: Flavio Dell'Acqua, Maxime Descoteaux, Alexander Leemans
Language: English
Hardback ISBN:
9 7 8 - 0 - 1 2 - 8 1 8 8 9 4 - 1
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 1 8 8 9 5 - 8

Handbook of Diffusion MR Tractography: Imaging Methods, Biophysical Models, Algorithms and Applications presents methods and applications of MR diffusion tractography, providing… Read more

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Handbook of Diffusion MR Tractography: Imaging Methods, Biophysical Models, Algorithms and Applications 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 modeling 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.

Handbook of Diffusion MR Tractography
Cover image
Title page
Table of Contents
Copyright
Contributors
Preface
Part I: From anatomy to tractography
Chapter 1 The brain and its pathways
Abstract
Keywords
Acknowledgments
1 Introduction
2 Overview of the nervous system
3 From molecules to neuronal circuits
4 Cytoarchitectonic and neuronal connectivity
5 White matter anatomy
6 Imaging networks with diffusion MRI tractography
7 Interhemispheric tract asymmetry during brain development and aging
8 Conclusions
References
Chapter 2 Neurobiology of connections
Abstract
Keywords
1 Introduction
2 The gray matter
2.1 Cortical areas
2.2 Cortical layers
2.3 Vertical organization
2.4 Gyrification
2.5 Development of gray matter and of gyri
2.6 Connections (outputs)
2.7 Connections (inputs)
2.8 Receptors
3 The white matter
4 The axon
4.1 Mapping
4.2 Differential amplification
4.3 Development of synaptic connections
4.4 Temporal transformations
4.5 Axon diameter in development
5 Conclusions
References
Chapter 3 Past and present of mapping brain connections
Abstract
Keywords
1 Early connectivity maps
2 Cortical mapping and connectivity
3 Associationist school and disconnection syndromes
4 Modern connectivity
5 Contemporary neuroimaging methods
6 Conclusions
References
Chapter 4 From Brownian motion to the brain connectome: My perspective and historical view of diffusion MRI
Abstract
Keywords
1 Introduction
2 The birth of diffusion MRI
2.1 The concept
2.2 The making
2.3 First trials
3 IVIM: Generalized diffusion MRI
4 How IVIM and diffusion MRI entered the clinical field
5 The emergence of DTI and tractography
5.1 Other NIH “firsts”
6 Water, the forgotten biological molecule
7 IVIM and diffusion fMRI
7.1 IVIM fMRI
7.2 Diffusion fMRI
8 A global picture for the brain connectome
9 Conclusion
References
Part II: Diffusion MRI
Chapter 5 Physics of diffusion imaging: Fundamentals
Abstract
Keywords
1 Diffusion basics
1.1 Langevin dynamics
1.2 Central limit theorem
1.3 Diffusion equation
1.4 Diffusion in the presence of tissue microstructure
2 How to measure diffusion with NMR?
2.1 Scales
2.2 The mesoscopic Bloch-Torrey equation
2.3 The diffusion-weighted signal
2.4 Pulsed gradients
3 Diffusion as coarse-graining
3.1 The double average
3.2 The three regimes
4 Coarse-graining over an axon
4.1 Axon caliber scale
4.2 Undulation scale
4.3 Axon as a “stick”
5 From microstructure to fiber orientations
5.1 White matter dMRI signal as a convolution
5.2 The Standard Model of diffusion in white matter
5.3 Factorizing fiber ODF and fascicle response: Rotational invariants
5.4 Specificity and relevance of SM parameters
6 Concluding remarks
References
Chapter 6 Diffusion MRI acquisition for tractography: Diffusion encoding
Abstract
Keywords
1 Diffusion contrast
1.1 Qualitative description of the origin of diffusion contrast
1.2 Quantitative description of the origin of diffusion contrast
2 The diffusion encoding block
2.1 Hardware for diffusion MRI
2.2 Choice of b-value and sampling scheme for tractography
2.3 Diffusion encoding related artifact reduction
2.4 Diffusion encoding adaptations
3 Terminology guide
References
Chapter 7 Diffusion MRI acquisition for tractography: Diffusion sequences
Abstract
Keywords
1 Introduction and a brief history
2 Single-shot diffusion sequences
3 Reduced sampling schemes
3.1 Parallel imaging
3.2 Partial Fourier acquisition
4 Segmented diffusion sequences
5 Simultaneous multislice and volumetric acquisitions
6 Imaging-related artifact reduction
6.1 Ghosting
6.2 Fat
6.3 Cerebrospinal fluid effects
6.4 Motion
6.5 Other artifacts
7 Multicontrast sensitization of the diffusion sequence
8 The trade-off between spatial and angular resolution in tractography acquisition
9 Terminology guide
References
Chapter 8 Diffusion MRI acquisition for tractography: Beyond the in vivo adult human brain
Abstract
Keywords
1 Ex vivo and preclinical diffusion imaging for brain tractography
2 Pediatrics, neonates, and fetal diffusion imaging
3 Diffusion imaging for tractography outside the brain
4 Terminology guide
References
Chapter 9 Diffusion MRI processing and estimation
Abstract
Keywords
1 Introduction
2 Correcting for artifacts
2.1 Data quality assessment
2.2 Signal drift
2.3 Gibbs ringing
2.4 Subject motion
2.5 Geometric distortions
2.6 Noise
2.7 Outliers
3 Estimation
3.1 DTI and DKI
3.2 Spherical deconvolution approaches
3.3 Multicompartment models
3.4 q-Space approaches
4 Conclusion
References
Chapter 10 Single-shell diffusion models: From DTI to HARDI
Abstract
Keywords
1 Introduction
2 From diffusion anisotropy to diffusion tensor imaging
2.1 Diffusion tensor imaging
2.2 Computing the diffusion tensor
2.3 Diffusion tensor metrics
2.4 Diffusion tensor orientation
2.5 Diffusion tensor limitations
3 High angular resolution diffusion imaging (HARDI)
3.1 Multitensor and multicompartmental approaches
3.2 Nonparametric approaches
3.3 Q-ball imaging
3.4 Persistent angular structure and diffusion orientation transform
4 Spherical deconvolution
4.1 Solving the deconvolution problem
4.2 Fiber response function
4.3 Constrained spherical deconvolution
4.4 Richardson-Lucy spherical deconvolution
5 Optimal strategies for single-shell models
6 Conclusion: Why single-shell?
References
Chapter 11 Multishell models
Abstract
Keywords
1 Introduction
2 What is multishell data?
3 Modeling of multishell data for fiber tracking
3.1 Models of diffusion
3.2 Models of fibrous tissue
4 Beyond multishell acquisitions
5 Summary and conclusion
References
Chapter 12 From diffusion models to fiber orientations
Abstract
Keywords
1 Introduction
2 Discrete fiber populations
3 Orientation distribution functions
3.1 Diffusion orientation distribution function
3.2 Fiber orientation distribution function
4 ODF representation
5 Extracting information from the ODF
5.1 Local fiber orientation
5.2 Extracting fiber-specific information and fixels
5.3 Fiber dispersion and structural complexity
6 Additional orientation distribution functions
6.1 Uncertainty ODF
6.2 Track orientation distribution function
7 Advanced processing of ODFs
8 fODFs from nondiffusion methods
9 Conclusion
References
Part III: Tractography algorithms
Chapter 13 Deterministic fiber tractography
Abstract
Keywords
1 Historical background
2 Key ingredients of fiber tractography
2.1 Overview
2.2 Mathematical framework of streamline FT
2.3 User-defined FT settings
2.4 Beyond streamlines
3 Methodological considerations
4 Conclusion
References
Chapter 14 Probabilistic tractography
Abstract
Keywords
1 Introduction
2 Orientation dispersion and uncertainty
3 The main idea of probabilistic propagation and tracking
4 Uncertainty-based probabilistic tractography
4.1 Bootstrapping
4.2 Bayesian inference
4.3 Uncertainty of what?
5 Dispersion-based probabilistic tractography
5.1 Streamline propagation
6 Comparing probabilistic trajectories
6.1 Synthetic data
6.2 In vivo data
7 Interpretation: What do these probabilistic estimates mean?
7.1 Confidence intervals
7.2 Proportion of estimated connecting fiber
8 Conclusion
References
Chapter 15 Geodesic tractography
Abstract
Keywords
Acknowledgments
1 Introduction
2 The Riemann-DTI paradigm
2.1 Heuristics
2.2 Mathematical formulation
2.3 Connecting geometry to DTI and DWI
2.4 The effect of data variability on geometry
3 Conclusion
Appendix: Basic concepts from linear algebra and tensor calculus
References
Chapter 16 Global tractography
Abstract
Keywords
Acknowledgments
1 Introduction
2 What does “global” really mean?
2.1 Can we ever be truly global?
3 The global tractography paradigm
3.1 “Generative” vs. “discriminative” approaches
4 Generative approaches
4.1 Spin glass models
4.2 Gibbs tracker
4.3 Multitissue and multicompartment models
4.4 Streamline-wise approaches
5 Discriminative approaches
5.1 Methods that filter streamlines
5.2 Methods that estimate streamline contributions
6 Extending the capabilities of global methods
6.1 Adding advanced models of tissue microstructure
6.2 Adding multimodal data
6.3 Adding anatomical information about white matter organization
7 Conclusion
References
Chapter 17 Machine learning in tractography
Abstract
Keywords
1 Introduction
2 Training and validation data
2.1 Hardware phantom, simulated, and ex vivo data
2.2 In vivo human datasets
3 Current applications of machine learning in tractography
3.1 Local modeling
3.2 Sequence-based modeling
3.3 Global modeling
3.4 Streamline classification
4 What could be gained, what did we learn, and what challenges remain?
4.1 General considerations on ML-based tractography
4.2 Considerations on the state of the art in ML-based tractography validation
4.3 Considerations on suitable datasets for ML-based tractography
5 Conclusion and take-home message
References
Chapter 18 Improving tractography using anatomical priors and multimodal integration
Abstract
Keywords
1 Introduction
2 Guiding tractography with tissue maps
2.1 Binary tissue masks
2.2 Probabilistic tissue maps
2.3 Cortical surfaces
3 Anatomical constraints
3.1 ROI-based tractography
3.2 Bundle-specific tractography
4 Tractography with microstructure information
4.1 Filtering tractograms with microstructure information
4.2 Guiding trajectories using microstructure information
5 Tractography with functional maps
5.1 Functional regions
5.2 Functional connectivity
5.3 Functional local orientation
6 Conclusion
References
Chapter 19 Tractography in pathological anatomy: Some general considerations
Abstract
Keywords
1 Introduction
2 Technical considerations for tractography within pathological brains
3 Practical considerations for tractography within pathological brains
3.1 Real-time tractography as a visualization and data exploration tool
3.2 The diffusion tensor imaging tractography pipeline
3.3 Tractography pipelines robust to crossing fibers
4 Challenges of implementing tractography pipelines in pathological brains
4.1 Semiautomatic and manual lesion filling
4.2 Atlas-based WM masking
5 What can I do with my tractogram in practice?
5.1 Interactive visualization
5.2 Virtual white matter bundle dissection
5.3 Connectomic analysis
6 Which tractography pipeline can I use in my application?
6.1 Disorders consisting of white matter lesions
6.2 Disorders consisting of cortical-based lesions
6.3 Disorders with marked brain anatomy distortion
6.4 Disorders with microscopic disease infiltrates and perilesional WM changes
7 Conclusion
Appendix
Acquisition information about the MS subject used for illustration purpose
Acquisition information about the patients used for Figs. 2, 8, and 10
References
Chapter 20 Tractography visualization
Abstract
Keywords
1 Introduction
2 Tractography rendering from a scientific visualization point-of-view
2.1 The many flavors of streamline rendering
2.2 Leveraging the graphical processing unit
3 Interacting with tractograms
3.1 Selecting streamlines with regions of interest
3.2 Interactively slicing tractograms via selection planes
3.3 Real-time tractography
3.4 Placing tractography in context
4 Advanced tractography visualization
4.1 Along-tract mapping and profiling
4.2 Web-based visualization
4.3 Uncertainty mapping
4.4 Immersive virtuality
4.5 Photorealistic rendering
5 Conclusion
References
Part IV: From streamlines to tracts
Chapter 21 Dissecting white matter pathways: A neuroanatomical approach
Abstract
Keywords
1 Principles of anatomically guided manual dissections
1.1 Optimal diffusion maps for ROI delineation
1.2 Tractography in clinical populations—Stroke and neurosurgical patients
1.3 Anatomical placement of ROIs
2 Anatomical delineation in nonhuman primates
3 Extracting statistical indices
4 Atlas of neuroanatomical dissections
4.1 Superior longitudinal fasciculus
4.2 Cingulum
4.3 Uncinate fasciculus
4.4 Inferior longitudinal fasciculus
4.5 Inferior fronto-occipital fasciculus
4.6 Arcuate fasciculus
4.7 Frontal aslant tract
4.8 Fronto-insular tracts
4.9 Corticospinal tract
4.10 Anterior thalamic radiations
4.11 Frontostriatal projections
4.12 Precentral and postcentral U-shaped fibers
4.13 Optic radiation (geniculocalcarine fasciculus)
4.14 Medial occipital longitudinal tract
4.15 Corpus callosum
4.16 Anterior commissure
4.17 Frontomarginal and fronto-orbito-polar tracts
4.18 Fornix
4.19 Vertical occipital fasciculus
4.20 Accumbofrontal pathway
Funding
References
Chapter 22 Dissecting white matter pathways: Automatic and semiautomatic approaches
Abstract
Keywords
1 Introduction
2 Why is it important?
3 What is a streamline?
4 Streamline distance functions
4.1 Comparing distances
5 Overview of state-of-the-art methods
5.1 Unsupervised and semisupervised methods
5.2 Supervised methods
6 Shape similarities using bundle adjacency and fractal dimensions
6.1 The fractal dimension of a bundle mask
6.2 Streamline-based bundle atlases
7 The impact of different pipeline choices
8 Visualizing the virtual dissections
9 Summary
References
Chapter 23 Methods and statistics for diffusion MRI tractometry
Abstract
Keywords
Acknowledgments
1 Introduction
2 Tractometry: Along-streamline analysis
2.1 Bundle segmentation
2.2 Streamline ordering
2.3 Core streamline definition
2.4 Measure assignment
3 Statistical analysis
3.1 Choice of space for along-streamline analysis
3.2 Statistical tests for along-streamline analysis
3.3 Normative modeling and machine learning
4 Comparison with other frameworks
5 Conclusion
References
Chapter 24 Connectivity and connectomics
Abstract
Keywords
1 Introduction
2 Delineating nodes
2.1 What is a brain area?
2.2 Connectivity fingerprints
2.3 Brain parcellation—Why parcellate the brain?
2.4 Brain parcellation—The methods
2.5 Connectivity-based parcellation
2.6 Multimodal parcellation
2.7 Gradients and soft parcellations
2.8 Looking forward
3 Mapping connectivity and connectomes
3.1 Deep white matter tracking
3.2 Path termination and superficial white matter tracking
3.3 Quantifying edges
3.4 Quantification biases
3.5 Summary
4 Connectome accuracy and validation
4.1 In vivo validation
4.2 Connectome phantoms
4.3 Other evidence of connectome validity
4.4 Connectome thresholding
4.5 Connectome filters
4.6 Summary and future directions
5 Graph analysis of the connectome
5.1 Building a graph model
5.2 Analyzing brain network connectivity
5.3 Analyzing brain network topology
5.4 The importance of null models
5.5 Summary
References
Chapter 25 Tractography validation Part 1: Foundations, numerical simulations, and phantom models
Abstract
Keywords
1 Anatomy, tractography, and validation
1.1 Anatomical length scales
1.2 Neuroimaging length scales
1.3 What needs to be validated?
1.4 Bridging anatomy and tractography
2 Numerical simulations
2.1 Simulating the brain network
2.2 Simulating tissue microstructure along the brain network
3 Physical phantoms
3.1 Isotropic liquids
3.2 Anisotropic phantoms for diffusion tractography
3.3 Which phantoms for which experiment?
References
Chapter 26 Tractography validation Part 2: The use of anatomical model systems and measures for validation
Abstract
Keywords
1 Anatomical model systems
1.1 Species and validation considerations
1.2 Anatomical model systems: Microdissection
1.3 Anatomical model systems: Neuronal tracers
1.4 Anatomical model systems: Validating fiber orientation
2 Empirical validations
2.1 What is empirical validation?
2.2 What can be assessed?
2.3 Empirical validation: Connectomes
2.4 Empirical validation: Fiber bundles
2.5 Empirical validation: Local reconstruction
2.6 Considerations in empirical validation
3 Measures for validation of tractography
3.1 Measures for validating connections/connectomes
3.2 Measures for validating bundles
3.3 Measures for validation of local fiber orientation distribution
3.4 Measures for validating microstructure
3.5 Quantification considerations
References
Chapter 27 Tractography validation part 3: Lessons learned through validation studies
Abstract
Keywords
1 Anatomy can be more complex than our models
2 Reconstruction techniques capture fiber orientation distribution but are limited in extracting discrete peaks and orientation
3 Tractography can reconstruct known WM anatomy—Valid path, shape, and position of WM bundles
4 Tractography is a fair, but far from perfect, predictor of the presence of connections: There is an inherent sensitivity/specificity trade-off
5 The strength of connections has useful predictive power
6 Methods are reproducible, but there is significant variance across methods
7 There exists no “optimal” combination of acquisition, reconstruction, or tractography parameters
8 Limitations: Obstacles, biases, and challenges to overcome
9 Bridging anatomy and tractography is challenging
9.1 Bridging anatomy and tractography
9.2 Validation as an iterative process
References
Chapter 28 Current challenges and opportunities for tractography
Abstract
Keywords
1 The rise of tractography in neuroscience
2 Virtual reconstruction versus underlying anatomy: What is and what isn’t?
2.1 Local signal challenges
2.2 Path generation challenge
2.3 Origin and termination
2.4 Tractography and spatial coverage
3 Pathways and connectomes: How to interpret them?
3.1 Bundle segmentation
3.2 Effects on connectomics
4 Challenges and opportunities for the dMRI tractography community
4.1 Nomenclature and terminology
4.2 Reaching consensus on anatomical definitions
4.3 Limitations in anatomical validation
5 Conclusion
References
Part V: Tractography applications
Chapter 29 Tractography: Applications to neurodevelopment, aging, and plasticity
Abstract
Keywords
1 Introduction
1.1 Tractography measures
1.2 Types of analysis
1.3 Confounds
1.4 Study cohorts and design
2 Brain development
2.1 Contributions of tractography
2.2 In utero development of white matter tracts
2.3 Understanding differential maturation in overlapping tracks
2.4 Graph theory analysis
3 Aging
3.1 Contributions of tractography
3.2 Cognitive associations
4 Sex differences
5 Atypical populations
5.1 Developmental disorders
5.2 Cerebrovascular and Alzheimer's disease
5.3 Modifiable risk factors
6 Plasticity
6.1 Cognitive development is correlated with white matter properties
6.2 Long-term impacts of childhood experience_ White matter development in expert musicians
6.3 The causal influence of environmental factors on white matter development: Intervention studies
7 Conclusions and future directions
References
Chapter 30 Linking behavior with white matter networks
Abstract
Keywords
1 Introduction
2 Socioemotional functions
2.1 White matter tracts and socioemotional processing in a healthy population
2.2 Frontal and limbic networks and impairments in social-emotional processing
3 Cognitive functions
3.1 Limbic white matter tracts and episodic memory in healthy populations
3.2 Limbic white matter tracts and impairments in episodic memory
3.3 White matter tracts and cognitive control in a healthy population
3.4 White matter tracts and impairments in cognitive control
4 Language functions
4.1 Arcuate fasciculus and language functions in a healthy population
4.2 Arcuate fasciculus and language impairments
5 Motor functions
6 Visuospatial functions
6.1 Frontoparietal white matter tracts and visuospatial functions in a healthy population
6.2 Frontoparietal white matter tracts in unilateral spatial neglect
7 Conclusions
References
Chapter 31 Neurosurgical applications of clinical tractography
Abstract
Keywords
1 Introduction
2 Neuro-oncology
3 Intraoperative tractography
4 Functional neurosurgery
5 Neurovascular
6 Skull base
7 Pediatrics
8 Traumatic brain injury
9 Conclusions
References
Chapter 32 Preclinical and ex vivo tractography: Techniques and applications at high field
Abstract
Keywords
1 Introduction
2 Technical considerations
2.1 Acquisition pulse sequences
2.2 q-space sampling schemes
3 Applications
3.1 Mesoscale connectivity mapping in the human brain
3.2 Microimaging and preclinical applications
3.3 Comparison of diffusion modeling and tractography approaches
4 Towards validation: Comparison with neuronal tracing, microscopy, and optical imaging modalities
5 Conclusion
References
Chapter 33 Multicenter studies and harmonization: Problems, solutions, and open challenges
Abstract
Keywords
1 Introduction
2 The problem
2.1 Quantifying variability
2.2 Sources of variability
2.3 Magnitude of variability
3 Proposed solutions
3.1 Statistical approaches for harmonization of diffusion MRI measures
3.2 Harmonization of DWI data
4 Open challenges
4.1 Multicenter variability of microstructural estimates
4.2 Fiber direction estimates and tractography
4.3 Disease
4.4 Longitudinal analysis
4.5 Evaluation of harmonization
5 Conclusion
References
Appendix A Vectors and tensors
A.1 Vectors
A.1.1 Mathematics of the vector
A.2 Tensors
A.2.1 Mathematics of the tensor
A.2.2 Applications of tensors in dMRI tractography
Appendix B Numerical integration
B.1 Introduction
B.2 Methods
B.2.1 The Euler method
B.2.2 The Runge-Kutta method
B.2.3 The Adams-Bashforth method
B.3 Aspects of numerical integration
B.4 Applications and relevance in tractography
References
Appendix C Interpolation, splines, and smoothing
C.1 Data interpolation
C.1.1 Diffusion image interpolation
C.2 Spline smoothing
References
Appendix D Spherical harmonics
D.1 Definition and properties of spherical harmonics
D.2 Spherical harmonics as a basis
D.3 Applications and relevance in tractography
References
Index

Flavio Dell'Acqua

Flavio Dell'Acqua is Associate Professor and Reader in Translational Neuroimaging at the Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK. A biomedical engineer and neuroscientist, Dr. Dell'Acqua's research interests span MR physics and medical image analysis. His work focuses on developing and applying advanced diffusion imaging and tractography methods for neuroscience, psychiatric, and clinical research. Dr. Dell'Acqua has co-authored over 100 papers, and his methods have been successfully applied in numerous tractography studies published in high-impact journals including Science, Nature Neuroscience, Nature Communication, Brain, and PNAS. He is a co-founder of the NatBrainLab, a multidisciplinary laboratory dedicated to the study of human neuroanatomy and tractography research. Committed to education in the field, Dr. Dell'Acqua has led educational courses on Diffusion Imaging for the Organization for Human Brain Mapping (OHBM) for multiple years. He is an active member of the International Society for Magnetic Resonance in Medicine (ISMRM) where he has lectured in educational courses, workshops and has served on the Diffusion Study Group committee. In 2023, he was a co-founder of the International Society for Tractography (IST).

Affiliations and expertise

Associate Professor in Translational Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK

Maxime Descoteaux

The investigator Maxime Descôteaux is a member of the Medical Imaging Axis of the Centre de recherche du CHUS, professor of the Computer Science Department, Faculty of Science, Université de Sherbrooke, Director of the Imaging and Visualization Platform (PAVI, pavi.dinf.usherbrooke.ca) and Director of the Sherbrooke Connectivity Imaging Laboratory (SCIL, scil.dinf.usherbrooke.ca). He is a leader in diffusion Magnetic Resonance Imaging (MRI) acquisition, processing and visualization to infer white matter connectivity of the brain. The goal of his research is to develop state-of-the-art fiber tractography tools to better understand functional coupling between cortical and sub-cortical regions of the brain and study connectivity properties of white matter to characterize fiber integrity. This field is now named ‘’connectomics’’. His most important contributions are in the fields of fundamental diffusion MRI processing and white matter fiber tract reconstruction and visualization. Recently, his tools have made the transfer to clinical applications for neurosurgical planning and interventions. He is a member of the editorial board of several important journals in neuroscience and co-founder of the company Imeka Solution Inc (www.imeka.ca).

Affiliations and expertise

Professor, Department of Computer Science, Faculty of Science, Université de Sherbrooke, Sherbrooke, Canada; Director, Imaging and Visualization Platform; Director, Sherbrooke Connectivity Imaging Laboratory

Alexander Leemans

Alexander Leemans is a physicist who received his Ph.D. in 2006 at the University of Antwerp, Belgium. From 2007 to 2009, he worked as a postdoctoral researcher at the Cardiff University Brain Research Imaging Center (CUBRIC), Cardiff University, Wales, United Kingdom. In 2009, he joined the Image Sciences Institute (ISI), University Medical Center Utrecht, the Netherlands, where he currently holds a tenured faculty position as Associate Professor. His current research interests include modeling, processing, visualizing and analyzing diffusion MRI data for investigating microstructural and architectural tissue organization. He heads the PROVIDI Lab and is the developer of ExploreDTI, which is a graphical toolbox for investigating diffusion MRI data

Affiliations and expertise

UMC Utrecht, Utrecht, The Netherlands

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Handbook of Diffusion MR Tractography

Imaging Methods, Biophysical Models, Algorithms and Applications

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Flavio Dell'Acqua

Maxime Descoteaux

Alexander Leemans