UCL Centre for Medical Image Computing Camino and DTI-TK: Advanced Diffusion MRI Pipeline for Traumatic Brain Injury Gary Hui Zhang, PhD Microstructure Imaging Group Centre for Medical Image Computing Department of Computer Science University College London 26th of June, 2013

Microstructure imaging with diffusion MRI

Signal

Diffusion MRI quantify water mobility in tissue

Virtual Histology Tissue

Cell size, shape, density Membrane permeability Orientation distribution

Tissue Modeling

Model parameters are the tissue microstructure feature themselves!

Axer, J. Neuro. Meth. 1999

Pipeline for advanced diffusion MRI analysis

Imaging Inference Localization Normalization

Pipeline for advanced diffusion MRI analysis

Imaging Inference Localization Normalization

Camino: a platform for advanced diffusion MRI analysis • Implements a rich hierarchy of analytic models for diffusion MRI • Provides a robust framework for fitting diffusion MRI data to the models • Delivers a sophisticated simulator for validating diffusion MRI models

Monte Carlo Diffusion Simulator

(Hall and Alexander, IEEE TMI 2009)

Diffusion Substrate Displacement PDF Diffusion MR Signal Gamma-Distributed Radii Crossing Cylinders Permeable Cylinders Mesh-based substrates

Rich hierarchy of analytic models of diffusion MRI

(Panagiotaki et al, NeuroImage 2012)

Stick Compartment Models Ball Astrosticks Cylinder Zeppelin Astrocylinders GDRCylinders Tensor Sphere Dot Multi-Compartment Models ZeppelinStickAstrosticks

Mapping axon diameter and density in the living human brain with ActiveAx

(Alexander et al, NeuroImage 2010)

Fixed tissue: • Vervet monkey • 4.7T; 140mT/m In vivo: • human volunteer • 3T; 60mT/m

Mapping neurite orientation dispersion and density with NODDI

(Zhang et al, NeuroImage 2012) Dominant Orientation 0 DTI Fractional Anisotropy 1 0 Orientation Dispersion 1 0 NODDI Neurite Density 1 0 CSF 1 The acquisition protocol is simple to implement and clinically feasible.

Neurite density: a potential imaging marker for brain recovery

(Wang et al, PLoS One 2013) NODDI enables the extension of this animal model study to living human subjects.

Pipeline for advanced diffusion MRI analysis

Imaging Inference Localization Normalization

Diffusion MRI supports superior anatomical alignment of white matter structures

T1 DTI ?

Corpus Callosum Optic Radiation Arcuate Fasciculus

DTI-TK provides the state-of-the-art for aligning diffusion MRI data • Ranked the best performing tool of its kind

(Wang et al, NeuroImage 2011)

• Supports unbiased longitudinal analysis of diffusion MRI data

(Keihaninejad et al, NeuroImage 2013)

The importance of tensor-based alignment for longitudinal processing

(Keihaninejad et al, NeuroImage 2013) Tensor-based alignment improves specificity

The importance of tensor-based alignment for longitudinal processing

(Keihaninejad et al, NeuroImage 2013) Tensor-based alignment improves sensitivity

Pipeline for advanced diffusion MRI analysis

Imaging Inference Localization Normalization

Tract-specific analysis with DTI-TK

(Yushkevich et al, NeuroImage 2008; Zhang et al, Medical Image Analysis 2010)

• Evaluate specific

a priori

hypotheses (e.g., ALS impairs only motor tracts) • Reduce confounding effect of neighboring structures • Present findings in the context natural to the structure

Typical Voxelwise Analysis Tract-Specific Analysis

Summary • Camino provides a rigorous platform for • developing and validating advanced diffusion MRI methods • applying these methods to routine clinical research and practice • DTI-TK supports population-based analysis of diffusion MRI data by • implementing the state-of-the-art spatial normalization tool • delivering a statistical inference tool tailored specifically for white matter • Together, they deliver an end-to-end pipeline for advanced diffusion MRI analysis

Acknowledgement • Colleagues at • CMIC and MIG (UCL) • Penn Image Computing and Science Laboratory (U Penn) • Camino funding support • EU CONNECT consortium ( www.brain-connect.eu

) • MS Society of Great Britain and Northern Ireland • UCLH Biomedical Research Centre funded by NIHR • DTI-TK funding support • NIH-NIBIB R03-EB009321 • NIH-NINDS R01-NS065347