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Diffusion Tensor Imaging II: Techniques and applications
Jennifer Campbell
• scalar maps from diffusion imaging • connectivity analysis • complex subvoxel fiber microstructure • diffusion contrast for functional imaging?
Diffusion Tensor Imaging II: Techniques and applications
Diffusion Imaging
Tissue structures determine which directions of motion are most probable
P(r | r0,! d) =1
| D | (4"! d)3• exp (r # r0)T D#1(r # r0)
4! d
$%&
'()
The diffusion tensor
Dxx Dxy Dxz
Dyx Dyy Dyz
Dzx Dzy Dzz
!
"
###
$
%
&&&
λ1
λ3 λ2
e1
Maps obtainable from the diffusion tensor
RGB plot: principal eigenvector (e1)
direction, scaled by FA
trace of diffusion tensor
(mean diffusivity)
anisotropy index: fractional anisotropy (FA)
anisotropy index: fractional anisotropy (FA)
trace of diffusion tensor
(mean diffusivity)
RGB plot: principal eigenvector (e1)
direction, scaled by FA
FA = 32
(!1" !)2 + (! 2 " !)2 + (! 3" !)2
(!12 + !2
2 + ! 32 )
MD = ! = trace / 3 = !1 + ! 2 + ! 3
3
Maps obtainable from the diffusion tensor
• scalar maps from diffusion imaging • connectivity analysis • complex subvoxel fiber microstructure • diffusion contrast for functional imaging?
Diffusion Tensor Imaging II: Techniques and applications
Applications: Stroke
• Moseley (1990): ADC decreases in ischemia. • Causes: cell membrane permeability decreases lead to cellular swelling
(currently debate about exact mechanism) • Early diagnosis: diffusion MRI can identify regions of ischemia within
30 minutes of arterial occlusion, while T2 images show changes only after 2 hours
• Chronic stroke can also cause Wallerian degeneration, leading to reduced FA
Applications: Multiple Sclerosis T2 FA
Bammer et al, MRM 2000
PD (left); T1 (right)
MD (left); FA (right)
increased MD and reduced FA in halo around ring-enhancing lesion
Applications: Multiple Sclerosis
Applications: Cancer • ADC higher in cysts and cystic components of tumours,
where diffusion is free. Can be used to differentiate cysts from solid tumours (difficult to differentiate with T1, T2)
Kono K et al. AJNR Am J Neuroradiol 2001;22:1081-1088
• tumour grading • examine white matter tracts
proximal to tumours; infiltration vs. displacement
• evaluation of treatment of cancer: ADC is lower in radiation damaged tissue
DW ADC
Applications: Dyslexia
• Subjects with reading difficulty exhibited decreased FA bilaterally in temporo-parietal areas involved in visual, auditory, and language processing
Klingsberg et al, Neuron 2000
Tract-based spatial statistics (TBSS)
Smith et al., NIMG 2006
• reduces problems associated with, e.g., partial volume averaging, misalignment
• project highest FA value in region (“centre of tract”) onto average FA skeleton
• scalar maps from diffusion imaging • connectivity analysis • complex subvoxel fiber microstructure • diffusion contrast for functional imaging?
Diffusion Tensor Imaging II: Techniques and applications
Diffusion MRI Tractography
Diffusion MRI Tractography
Diffusion MRI Tractography
human, postmortem Klinger method
nonhuman, tracer injection
human, in vivo
Tract delineation
tractography constraints: Calamante et al. NeuroImage 2010.
A: inclusion or stopping mask based on thresholding a scalar map, e.g., fractional anisotropy (FA)
Tract delineation
tractography constraints: Calamante et al. NeuroImage 2010.
B: tract-delineating regions of interest (ROIs):
Tract delineation
tractography constraints: Calamante et al. NeuroImage 2010.
B: tract-delineating regions of interest (ROIs): - may be intermediate points or expected end points -“way points” or “obligatory passages”
Tract delineation
tractography constraints: Calamante et al. NeuroImage 2010.
C: exclusion masks
Tract delineation
tractography constraints: Calamante et al. NeuroImage 2010.
C: exclusion masks
Tract delineation
tractography constraints: Calamante et al. NeuroImage 2010.
B: tract-delineating regions of interest (ROIs): - may be intermediate points or expected end points -“way points” or “obligatory passages”
Tract delineation
Calamante et al. NeuroImage 2010.
tractography constraints: B: tract-delineating regions of interest (ROIs): - may be intermediate points or expected end points -“way points” or “obligatory passages”
Tract delineation
tractography constraints: Calamante et al. NeuroImage 2010.
D: curvature constraints
Tract delineation
tractography constraints: Calamante et al. NeuroImage 2010.
D: curvature constraints E: other inclusion or exclusion criteria, e.g., tract length
Tractography of major white matter fasciculi
Catani et al. Cortex 2008.
Tractography: warnings • Diffusion MRI tractography results include many false
positives and false negatives: priors are essential. – Tractography is good for segmentation of parts of pathways, but
segmenting the entire pathway can be challenging (false negatives). – Characterizing unknown new anatomy is much more difficult than
studying known anatomy (false positives). – Example: Thomas et al. (Brain, 2005) report increase in fiber count
in corticobulbar tract on unaffected hemisphere in cerebral palsy: is this due to reorganization, or is this tract easier to trace due to the absence of callosal inputs from the affected hemisphere?
• Tractography does not distinguish between afferent and efferent pathways, or between mono- and multisynaptic connections.
Probabilistic tractography: incorporating uncertainty
Jeurissen et al. HBM 2010.
Probabilistic tractography: incorporating uncertainty
Tract delineation techniques • define tract delineating
ROIs manually with well-defined protocol (e.g., Mori et al., 2005; Wakana et al., 2007, Catani et al., 2008)
• define tract delineating ROIs using fMRI activations (e.g., Powell et al., 2006; Ghaderi et al., ISMRM 2012)
• define tract delineating ROIs automatically (e.g., using population atlas - Rose et al. 2010)
Catani et al. Cortex 2008.
Tract delineation techniques • define tract delineating
ROIs manually with well-defined protocol (e.g., Mori et al., 2005; Wakana et al., 2007, Catani et al., 2008)
• define tract delineating ROIs using fMRI activations (e.g., Powell et al., 2006; Ghaderi et al., ISMRM 2012)
• define tract delineating ROIs automatically (e.g., using population atlas - Rose et al. 2010)
Powell et al. NeuroImage 2006.
Ghaderi et al., ISMRM 2012
Tract delineation techniques • define tract delineating
ROIs manually with well-defined protocol (e.g., Mori et al., 2005; Wakana et al., 2007, Catani et al., 2008)
• define tract delineating ROIs using fMRI activations (e.g., Powell et al., 2006; Ghaderi et al., ISMRM 2012)
• define tract delineating ROIs automatically (e.g., using population atlas - Rose et al. 2010)
Rose et al. Brain Connectivity 2011.
What is tractography useful for? • 3D visualization aid:
localization, education, investigation
• tool to determine region in which to analyze scalar maps
• tool to investigate tract-specific properties, e.g., tract length, tract count, tract asymmetry, network properties
• segmentation of neuroanatomical structures based on connectivity
• investigation of fiber anatomy
What is tractography useful for? • 3D visualization aid:
localization, education, investigation
• tool to determine region in which to analyze scalar maps
• tool to investigate tract-specific properties, e.g., tract length, tract count, tract asymmetry, network properties
• segmentation of neuroanatomical structures based on connectivity
• investigation of fiber anatomy
Assaf et al, 2011
Tractometry
What is tractography useful for? • 3D visualization aid:
localization, education, investigation
• tool to determine region in which to analyze scalar maps
• tool to investigate tract-specific properties, e.g., tract length, tract count, tract asymmetry, network properties
• segmentation of neuroanatomical structures based on connectivity
• investigation of fiber anatomy
Luck et al, NIMG 2010
What is tractography useful for? • 3D visualization aid:
localization, education, investigation
• tool to determine region in which to analyze scalar maps
• tool to investigate tract-specific properties, e.g., tract length, tract count, tract asymmetry, network properties
• segmentation of neuroanatomical structures based on connectivity
• investigation of fiber anatomy
Tract Asymmetry Index (AI): AI = (C - I) / (C + I)
Rose et al., Brain Connectivity 2011
What is tractography useful for? Connectomics
Bastiani et al., NIMG 2012
Network properties: • small worldedness • network efficiency • hub location
• 3D visualization aid: localization, education, investigation
• tool to determine region in which to analyze scalar maps
• tool to investigate tract-specific properties, e.g., tract length, tract count, tract asymmetry, network properties
• segmentation of neuroanatomical structures based on connectivity
• investigation of fiber anatomy
What is tractography useful for? • 3D visualization aid:
localization, education, investigation
• tool to determine region in which to analyze scalar maps
• tool to investigate tract-specific properties, e.g., tract length, tract count, tract asymmetry, network properties
• segmentation of neuroanatomical structures based on connectivity
• investigation of fiber anatomy Johansen-Berg et al., Nature 2003.
Thalamic segmentation
What is tractography useful for? • 3D visualization aid:
localization, education, investigation
• tool to determine region in which to analyze scalar maps
• tool to investigate tract-specific properties, e.g., tract length, tract count, tract asymmetry, network properties
• segmentation of neuroanatomical structures based on connectivity
• investigation of fiber anatomy
Schmamann et al. Brain 2007.
Applications: Epilepsy
Yogarajah et al. NeuroImage 2008.
Findings:
decreased left intratract FA in L TLE
22% reduction in left tract volume in L TLE
Applications: Multiple Sclerosis
Stikov et al, ISMRM 2013
• scalar maps from diffusion imaging • connectivity analysis • complex subvoxel fiber microstructure • diffusion contrast for functional imaging?
Diffusion Tensor Imaging II: Techniques and applications
Probability of water displacement orientation distribution function (ODF)
Probability of water displacement orientation distribution function (ODF)
Diffusion tensor model
High Angular Resolution Diffusion Imaging (HARDI)
QBI DTI
High Angular Resolution Diffusion Imaging (HARDI)
Crossing fiber reconstruction approaches • multi-tensor approaches (Alexander et al., Parker et al.,
others)
• Mixture models (Gaussian (Tuch et al.); Wishart (Jian et
al.))
• ball and multi-stick (Behrens et al.)
• diffusion spectrum imaging (DSI) (Wedeen et al.)
• q-ball imaging (QBI) (Tuch et al.)
• Composite hindered and restricted model of diffusion (CHARMED) (Assaf et al.)
• spherical deconvolution (Tournier et al., • Anderson, others) • other variants…
Multi-tensor model
Hosey et al. 2008
Tuch et al. 2002
Behrens’ ball and stick model
Behrens et al. 2007
Diffusion weighted MRI sequence TE
G
90° 180° echo
G
ΔδΔ
δ b = ! 2G2" 2 (# $" / 3)
• b value indicates the magnitude of the diffusion weighting:
q=γGδ
Diffusion Spectrum Imaging (DSI)
3D diffusion pdf
2D diffusion Orientation Distribution Function
(ODF)
Wedeen et al. ; Canales-Rodrigue et al.
q-ball imaging
Tuch et al. MRM 2004
• direct calculation of the 2D diffusion ODF
• calculation uses Funk-Radon transform
Composite hindered and restricted model of diffusion
(CHARMED)
restricted compartment
Crossing fiber detection: QBI vs. Deconvolution
QBI diffusion ODF Deconvolved fiber ODF
Tournier et al. NIMG 2008.
Savadjiev et al. NeuroImage 2008.
Beyond crossing: other complex subvoxel geometries
Curve Inference to distinguish fanning from bending fibers
subvoxel fanning of fibers
Campbell et al. ISMRM 2011.
Savadjiev et al. NeuroImage 2008.
Beyond crossing: other complex subvoxel geometries
Tractography: warnings • Diffusion MRI tractography results include many false
positives and false negatives: priors are essential. – Tractography is good for segmentation of parts of pathways, but
segmenting the entire pathway can be challenging (false negatives). – Characterizing unknown new anatomy is much more difficult than
studying known anatomy (false positives). – Example: Thomas et al. (Brain, 2005) report increase in fiber count
in corticobulbar tract on unaffected hemisphere in cerebral palsy: is this due to reorganization, or is this tract easier to trace due to the absence of callosal inputs from the affected hemisphere?
• Tractography does not distinguish between afferent and efferent pathways, or between mono- and multisynaptic connections.
Tractography: warnings • Diffusion MRI tractography results include many false
positives and false negatives: priors are essential. – Tractography is good for segmentation of parts of pathways, but
segmenting the entire pathway can be challenging (false negatives).
Tractography: warnings • Diffusion MRI tractography results include many false
positives and false negatives: priors are essential. – Tractography is good for segmentation of parts of pathways, but
segmenting the entire pathway can be challenging (false negatives). – Characterizing unknown new anatomy is much more difficult than
studying known anatomy (false positives).
Frey et al. 2006
Diffusion imaging of tissue microstructure
• intra-axonal: anisotropically restricted
• extracellular: hindered (anisotropically in fiber bundles)
• cell bodies: isotropically restricted
• CSF: not restricted or hindered 10 µm
• AxCaliber (Assaf et al.) • CHARMED (Assaf et al.) • ActiveAx (Alexander et al., Zhang et al.) • NODDI (Zhang et al.) • Jespersen model • restriction spectrum imaging (White and
Dale) • diffusion basis spectrum imaging (Wang et
al.)
Diffusion imaging of tissue microstructure
q-space analysis of diffusion MRI data
McNab et al. ISMRM 2012
AxCaliber Aboitiz et al. (1993)
Composite hindered and restricted model of diffusion
(CHARMED)
restricted pool fraction F fractional anisotropy FA
NODDI: Neurite orientation dispersion
and density imaging • a model of cellular structure that allows for complex subvoxel
fiber geometry (splay, curvature) • estimated parameters include
• intra-axonal (anisotropically restricted) signal fraction • extra-axonal (anisotropically hindered) signal fraction • isotropic signal fraction (CSF)
Zhang et al. NeuroImage 2012
NODDI: Neurite orientation dispersion
and density imaging
Axon volume fraction (AVF)
Diffusion imaging of tissue microstructure
courtesy Nikola Stikov
g-ratio = d / D
= 1/ (1+ MVF / AVF)
d D
21 µm
Diffusion imaging of tissue microstructure
Axon volume fraction (AVF)
g-ratio
Diffusion imaging of tissue microstructure
• scalar maps from diffusion imaging • connectivity analysis • complex subvoxel fiber microstructure • diffusion contrast for functional imaging?
Diffusion Tensor Imaging II: Techniques and applications
normal tissue
Diffusion tensor imaging (DTI)
cellular swelling: • MD decreases
Diffusion tensor imaging (DTI)
Diffusion contrast for functional imaging?
Le Bihan et al. PNAS 2006
• stroke • MS • cancer • trauma • dyslexia • epilepsy • schizophrenia • drug effects
Applications of diffusion MRI
• ALS • dementia • CJD • cerebral palsy • blindsight • depression • therapy outcome • meditation
• neuroanatomy • development • aging • surgical planning • surgical outcome • plasticity • phenotype
characterization
Software packages • FSL FDT (FMRIB, Oxford) • DTI Studio (Johns Hopkins) • Camino (Manchester/London)
• CINCH (Stanford)
• Mrtrix (Brain Research Institute, Melbourne) • ExploreDTI (Utrecht/Cardif)
• Diffusion Toolkit / TrackVis (Massachusetts General Hospital)
• BrainVISA (NeuroSpin et al.)
• MedInria (Inria) • DiPy • Fiber Navigator • more …