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ADC and ODF estimation from HARDI
Maxime Descoteaux1
Work done with E. Angelino2, S. Fitzgibbons2, R. Deriche1
1. Projet Odyssée, INRIA Sophia-Antipolis, France2. Physics and Applied Mathematics, Harvard University, USA
Max Planck Institute, March 28th 2006
Plan of the talk
Introduction
Spherical Harmonics Formulation
Applications:
1) ADC Estimation 2) ODF Estimation
Discussion
• DTI fails in the presence of many principal directions of different fiber bundles within the same voxel
• Non-Gaussian diffusion process
Limitation of classical DTI
[Poupon, PhD thesis]True diffusion
profileDTI diffusion
profile
High Angular Resolution Diffusion Imaging (HARDI)
• N gradient directions • We want to recover fiber crossings
SolutionSolution: Process all discrete noisy samplings on the sphere using high order formulations
162 points 642 points
High Order Descriptions
Seek to characterize multiple fiber diffusion• AApparent DDiffusion CCoefficient (ADCADC)• OOrientation DDistribution FFunction (ODFODF)
ADC profileFiber distribution Diffusion ODF
Sketch of the approachData on the
sphere
Spherical harmonic
description of data
ODF
For l = 6,
C = [c1, c2 , …, c28]
ADC
ADC ODF
Spherical harmonics
Description of discrete data on the sphere
Regularization of the coefficients
Spherical harmonicsformulation
• Orthonormal basis for complex functions on the sphere
• Symmetric when order l is even• We define a real and symmetric modified
basis Yj such that the signal
[Descoteaux et al. SPIE-MI 06]
Regularization with the Laplace-Beltrami ∆b
• Squared error between spherical function F and its smooth version on the sphere ∆bF
• SH obey the PDE
• We have,
Minimization & -regularization
• Minimize (CB - S)T(CB - S) + CTLC
=>C = (BTB + L)-1 BTS
• Find best with L-curve method• Intuitively, is a penalty for having higher order
terms in the modified SH series=> higher order terms only included when needed
Estimation of the ADC
Characterize multi-fiber diffusion
High order anisotropy measuresanisotropy measures
Apparent diffusion coefficient
ADC profile : D(g) = gTDgDiffusion MRI signal : S(g)
In the HARDI literature…
2 class of high order ADC fitting algorithms:
1) Spherical harmonic (SH) series[Frank 2002, Alexander et al 2002, Chen et al 2004]
2) High order diffusion tensor (HODT)[Ozarslan et al 2003, Liu et al 2005]
Summary of algorithm
High OrderDiffusion Tensor
(HODT)
SphericalHarmonic (SH)
Series
Modified SH basis Yj
C = (BTB + L)-1BTX
Least-squares with-regularization
D = M-1C
M transformation
HODT Dfrom linear-regression
[Descoteaux et al. SPIE-MI 06]
3 synthetic fiber crossing
Limitations of the ADC
• Maxima do not agree with underlying fibers• ADC is in signal space (q-space)
HARDI ADC profiles
Need a function that is in real space with maxima that agree with fibers => ODF
[Campbell et al., McGill University, Canada]
Analytical ODF Estimation
Q-Ball Imaging
Funk-Hecke Theorem
Fiber detection
Q-Ball Imaging (QBI) [Tuch; MRM04]
• ODF can be computed directly from the HARDI signal over a single ball
• Integral over the perpendicular equator = Funk-Radon Transform
[Tuch; MRM04]~= ODF
Illustration of the Funk-Radon Transform (FRT)
Diffusion Signal
FRT->
ODF
Funk-Hecke Theorem
[Funk 1916, Hecke 1918]
Recalling Funk-Radon integral
Funk-Hecke ! Problem: Delta function is discontinuous at 0 !
Funk-Hecke formula
Solving the FR integral:Trick using a delta sequence
=>
Delta sequence
Final Analytical ODF expression in SH coefficients
[Descoteaux et al. ISBI 06]
Biological phantom
T1-weigthed Diffusion tensors
[Campbell et al.NeuroImage 05]
Corpus callosum - corona radiata - superior longitudinal
FA map + diffusion tensors ODFs
Corona radiata diverging fibers - longitudinal fasciculus
FA map + diffusion tensors ODFs
Contributions & advantages
• SH and HODT description of the ADC• Application to anisotropy measures
• Analytical ODF reconstruction• Discrete interpolation/integration is eliminated
• Solution for all directions in a single step• Faster than Tuch’s numerical approach by a factor 15
• Spherical harmonic description has powerful properties• Smoothing with Laplace-Beltrami, inner products,
integrals on the sphere solved with Funk-Hecke
Perspectives
• Tracking and segmentation of fibers using multiple maxima at every voxel
• Consider the full diffusion ODF in the tracking and segmentation
Thank you!
Key references:• http://www-sop.inria.fr/odyssee/team/
Maxime.Descoteaux/index.en.html
-Ozarslan et al. Generalized tensor imagingand analytical relationships between diffusion tensorand HARDI, MRM 50, 2003
-Tuch D. Q-Ball Imaging, MRM 52, 2004
Classical DTI model
Diffusion profile : qTDqDiffusion MRI signal : S(q)
• Brownian motion P of water molecules can be described by a Gaussian diffusion process characterized by rank-2 tensor D (3x3 symmetric positive definite)
DTI-->
Spherical Harmonics (SH) coefficients
• In matrix form, S = C*BS : discrete HARDI data 1 x NC : SH coefficients 1 x m = (1/2)(order + 1)(order + 2)
B : discrete SH, Yjm x N(N diffusion gradients and m SH basis elements)
• Solve with least-square C = (BTB)-1BTS
[Brechbuhel-Gerig et al. 94]
Introduction
Limitations of Diffusion Tensor Imaging (DTI)
High Angular Resolution Diffusion Imaging (HARDI)
Our Contributions
• Spherical harmonics (SH) description of the data• Impose a regularization criterion on the solution
• Application to ADC estimation• Direct relation between SH coefficients and
independent elements of the high order tensor (HOT)• Application to ODF reconstrution
• ODF can be reconstructed ANALITYCALLY• Fast: One step matrix multiplication
• Validation on synthetic data, rat biological phantom, knowledge of brain anatomy
High order diffusion tensor (HODT) generalization
Rank l = 2 3x3
D = [ Dxx Dyy Dzz Dxy Dxz Dyz ]
Rank l = 4 3x3x3x3
D = [ Dxxxx Dyyyy Dzzzz Dxxxy Dxxxz Dyzzz Dyyyz Dzzzx Dzzzy Dxyyy Dxzzz Dzyyy Dxxyy Dxxzz Dyyzz ]
Tensor generalization of ADC
• Generalization of the ADC,
rank-2 D(g) = gTDg
rank-l
Independent elementsDk of the tensor
General tensor
[Ozarslan et al., mrm 2003]
Trick to solve the FR integral
• Use a delta sequence n approximation of the delta function in the integral• Many candidates: Gaussian of decreasing
variance
• Important property
n is a delta sequence
=>
1)
2)
3)
Nice trick!
=>
Spherical Harmonics
• SH
• SH PDE
• Real• Modified basis
Funk-Hecke Theorem
• Key Observation:• Any continuous function f on [-1,1] can be extended to
a continous function on the unit sphere g(x,u) = f(xTu), where x, u are unit vectors
• Funk-Hecke thm relates the inner product of any spherical harmonic and the projection onto the unit sphere of any function f conitnuous on [-1,1]
• Funk-Radon Transform
• True ODF
Funk-Radon ~= ODF
(WLOG, assume u is on the z-axis)
J0(2z)
z = 1 z = 1000
[Tuch; MRM04]
Field of Synthetic Data
90 crossing
b = 1500SNR 15order 6
55 crossingb = 3000
Time Complexity
• Input HARDI data |x|,|y|,|z|,N• Tuch ODF reconstruction:
O(|x||y||z| N k)
(8N) : interpolation point
k := (8N) • Analytic ODF reconstruction
O(|x||y||z| N R)
R := SH elements in basis
Time Complexity Comparison
• Tuch ODF reconstruction:• N = 90, k = 48 -> rat data set
= 100, k = 51 -> human brain= 321, k = 90 -> cat data set
• Our ODF reconstruction:• Order = 4, 6, 8 -> m = 15, 28, 45
=> Speed up factor of ~3
Time complexity experiment
• Tuch -> O(XYZNk)• Our analytic QBI -> O(XYZNR)
• Factor ~15 speed up
ODF evaluation
Tuch reconstruction vsAnalytic reconstruction
Tuch ODFs Analytic ODFs
Difference: 0.0356 +- 0.0145Percentage difference: 3.60% +- 1.44%
[INRIA-McGill]
Human Brain
Tuch ODFs Analytic ODFs
Difference: 0.0319 +- 0.0104Percentage difference: 3.19% +- 1.04%
[INRIA-McGill]
Synthetic Data Experiment
• Multi-Gaussian model for input signal
• Exact ODF
Limitations of classical DTI
Classical DTIrank-2 tensor
HARDIODF reconstruction
Strong Agreement
b-value
Average differencebetween exact ODFand estimated ODF
Multi-Gaussian model with SNR 35
SummarySignal S on the
sphere
Spherical harmonic
description of S
ODF
Physically meaningfulspherical harmonicbasis
Analytic solution usingFunk-Hecke formula
Fiber directions
ADCHigh order anisotropy measures