ADC and ODF estimation from HARDI

1. ADC and ODF estimation from HARDI Maxime Descoteaux1 Work done with E. Angelino2, S. Fitzgibbons2, R. Deriche1 1. Projet Odyssée, INRIA Sophia-Antipolis, France 2. Physics and Applied Mathematics, Harvard University, USA Max Planck Institute, March 28th 2006

2. Plan of the talk Introduction Spherical Harmonics Formulation Applications: 1) ADC Estimation 2) ODF Estimation Discussion

3. Limitation of classical DTI • DTI fails in the presence of many principal directions of different fiber bundles within the same voxel • Non-Gaussian diffusion process True diffusion profile DTI diffusion profile [Poupon, PhD thesis]

4. High Angular Resolution Diffusion Imaging (HARDI) 162 points 642 points • N gradient directions • We want to recover fiber crossings Solution: Process all discrete noisy samplings on the sphere using high order formulations

5. High Order Descriptions Seek to characterize multiple fiber diffusion • Apparent Diffusion Coefficient (ADC) • Orientation Distribution Function (ODF) ADC profile Diffusion ODF Fiber distribution

6. Sketch of the approach Data on the sphere For l = 6, C = [c1, c2 , …, c28] Spherical harmonic description of data ADC ODF ADC ODF

7. Spherical harmonics Description of discrete data on the sphere Regularization of the coefficients

8. 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]

9. 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,

10. Minimization & -regularization • Minimize (CB - S)T(CB - S) + CTLC => C = (BTB + L)-1BTS • 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

11. Estimation of the ADC Characterize multi-fiber diffusion High order anisotropy measures

12. Diffusion MRI signal : S(g) Apparent diffusion coefficient ADC profile : D(g) = gTDg

13. In the HARDI literature… 2 class of high order ADC fitting algorithms: • Spherical harmonic (SH) series [Frank 2002, Alexander et al 2002, Chen et al 2004] • High order diffusion tensor (HODT) [Ozarslan et al 2003, Liu et al 2005]

14. Summary of algorithm Spherical Harmonic (SH) Series High Order Diffusion Tensor (HODT) HODT D from linear-regression Modified SH basis Yj Least-squares with -regularization M transformation C = (BTB + L)-1BTX D = M-1C [Descoteaux et al. SPIE-MI 06]

15. 3 synthetic fiber crossing

17. Limitations of the ADC • Maxima do not agree with underlying fibers • ADC is in signal space (q-space) HARDI ADC profiles [Campbell et al., McGill University, Canada] Need a function that is in real space with maxima that agree with fibers => ODF

18. Analytical ODF Estimation Q-Ball Imaging Funk-Hecke Theorem Fiber detection

19. ODF can be computed directly from the HARDI signal over a single ball Integral over the perpendicular equator = Funk-Radon Transform Q-Ball Imaging (QBI) [Tuch; MRM04] [Tuch; MRM04] ~= ODF

20. FRT -> ODF Illustration of the Funk-Radon Transform (FRT) Diffusion Signal

21. Funk-Hecke Theorem [Funk 1916, Hecke 1918]

22. Funk-Hecke ! Problem: Delta function is discontinuous at 0 ! Recalling Funk-Radon integral

23. Funk-Hecke formula Delta sequence => Solving the FR integral:Trick using a delta sequence

24. Final Analytical ODF expression in SH coefficients  [Descoteaux et al. ISBI 06]

25. Biological phantom [Campbell et al. NeuroImage 05] T1-weigthed Diffusion tensors

26. Corpus callosum - corona radiata - superior longitudinal FA map + diffusion tensors ODFs

27. Corona radiata diverging fibers - longitudinal fasciculus FA map + diffusion tensors ODFs

28. 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

29. Perspectives • Tracking and segmentation of fibers using multiple maxima at every voxel • Consider the full diffusion ODF in the tracking and segmentation

30. Thank you! Key references: • http://www-sop.inria.fr/odyssee/team/ Maxime.Descoteaux/index.en.html -Ozarslan et al. Generalized tensor imaging and analytical relationships between diffusion tensor and HARDI, MRM 50, 2003 -Tuch D. Q-Ball Imaging, MRM 52, 2004

32. Classical DTI model DTI --> • Brownian motion P of water molecules can be described by a Gaussian diffusion process characterized by rank-2 tensor D (3x3 symmetric positive definite) Diffusion MRI signal : S(q) Diffusion profile : qTDq

33. Spherical Harmonics (SH) coefficients • In matrix form, S = C*B S : discrete HARDI data 1 x N C : SH coefficients 1 x m = (1/2)(order + 1)(order + 2) B : discrete SH, Yj(m x N (N diffusion gradients and m SH basis elements) • Solve with least-square C = (BTB)-1BTS [Brechbuhel-Gerig et al. 94]

34. Introduction Limitations of Diffusion Tensor Imaging (DTI) High Angular Resolution Diffusion Imaging (HARDI)

35. 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

36. 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 ]

37. Tensor generalization of ADC • Generalization of the ADC, rank-2 D(g) = gTDg rank-l General tensor Independent elements Dk of the tensor [Ozarslan et al., mrm 2003]

39. 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

40. n is a delta sequence 1) 2) =>

41. Nice trick! 3) =>

42. Spherical Harmonics • SH • SH PDE • Real • Modified basis

43. 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]

44. z = 1 z = 1000 J0(2z) [Tuch; MRM04] (WLOG, assume u is on the z-axis) Funk-Radon ~= ODF • Funk-Radon Transform • True ODF

45. 55 crossing b = 3000 Field of Synthetic Data b = 1500 SNR 15 order 6 90 crossing

46. Time Complexity • Input HARDI data |x|,|y|,|z|,N • Tuch ODF reconstruction: O(|x||y||z| N k) (8N) : interpolation point k := (8N) • Analytic ODF reconstruction O(|x||y||z| N R) R := SH elements in basis

47. 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

48. Time complexity experiment • Tuch -> O(XYZNk) • Our analytic QBI -> O(XYZNR) • Factor ~15 speed up

49. ODF evaluation

50. Tuch reconstruction vsAnalytic reconstruction Analytic ODFs Tuch ODFs Difference: 0.0356 +- 0.0145 Percentage difference: 3.60% +- 1.44% [INRIA-McGill]