1 / 32

Generalized Tensor-Based Morphometry (TBM) for the analysis of brain MRI and DTI

Generalized Tensor-Based Morphometry (TBM) for the analysis of brain MRI and DTI. Natasha Leporé, Laboratory of Neuro Imaging at UCLA. TBM o verview. Target. Source. TBM. TBM mathematical overview. Jacobian matrix (2D). Outline of talk. MRI: 1. Statistical Analysis

iman
Download Presentation

Generalized Tensor-Based Morphometry (TBM) for the analysis of brain MRI and DTI

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Generalized Tensor-Based Morphometry (TBM) for the analysis of brain MRI and DTI Natasha Leporé, Laboratory of Neuro Imaging at UCLA

  2. TBM overview Target Source

  3. TBM

  4. TBM mathematical overview Jacobian matrix (2D)

  5. Outline of talk MRI: 1. Statistical Analysis Nonlinear Registration Template selection DTI: 4. Extension to DTI

  6. TBM

  7. Volume vs shape changes (Lepore et al., TMI 2007) Usual TBM (Volume changes): But this does not take into account the direction of the changes… J = 0.5 0 0 2 So directional shrinkage and growth, but det(J) = 1 !

  8. Shape and volume statistics Multivariate statistics are computed on the 6 components of the deformation tensors  = (JTJ)1/2 Or more precisely, on their logarithm.

  9. Application to HIV/AIDS We are going to demonstrate our method using: ･26 HIV/AIDS patients + 14 controls ･Various kinds of statistics for 1. Volume changes 2. Volume and shape changes Permutation based statistics to avoid assuming a normal distribution.

  10. Changes in the corpus callosum maximum (λ1 , λ2) (λ1 , λ2) ( N11 , √ 2N12 , N22) with N = log , I identity matrix, u1 eigenvector , (λ1 , λ2) eigenvalues

  11. Volume and shape statistics for the whole brain Log p-values Log p-values Deformation Tensors Determinants

  12. TBM

  13. u vΔt2 vΔtn vΔt1 Fluid vs elastic registration But in fact … At each voxel, u(x,y) and v(x,y) = du/dt u analysis (elastic) and v analysis (fluid)

  14. Riemannian fluid registration (Brun et al., MICCAI 2007) F : Driving force from the intensity difference between images Similarity term Regularization term ???

  15. Building a Regularizer • The natural way to do the regularization in TBM is to use the deformation tensors, since they characterize the distortion of the local volume. • Since we are in the log-Euclidean framework, we want to use the matrix logarithms. • We want to use a fluid regularizer so we can have large deformations.

  16. Regularizer Elastic Registration (Pennec, 2005) Fluid Registration where ∑ v : rate of strain

  17. Riemannian fluid registration (Brun et al., MICCAI 2007) F : Driving force from the intensity difference between images Similarity term Regularization term

  18. Implementation: data • 23 pairs of identical twins 23 pairs of fraternal twins • 4T MRI scans, DTI 30 directions • Data bank: 1150 healthy twins (21-27 years old) MRI, HARDI and neuropsychological measures

  19. Statistics on twins Twin 1 Twin 2 Intraclass correlation: MSwithin MSbetween - MSwithin MSbetween + MSwithin ICC = MS: Mean square We use the ICC to compute the correlation of the deformation tensors (well, their determinants …) in twin pairs. MSbetween

  20. Accuracy of the Riemannian fluid registration method Image 1 Image 2 Difference btw warped image and initial image Image 2 registered to image 1

  21. Application of the Riemannian fluid method to genetic studies Percent mean absolute difference in regional volume Determinant of the Jacobian Tangent of the Geodesic Anisotropy Identical twins Fraternal twins

  22. Consistency of results: two fluid methods - genetic studies Significance of the Intraclass Correlation (ICC)

  23. TBM

  24. Template averaging (Lepore et al., MICCAI 2008) • Features are typically sharper in individual brain images than in mean anatomical templates • But, we want to eliminate bias from registration to one individual • Statistics are performed on deformation tensors

  25. Template averaging (Lepore et al., MICCAI 2008) • Features are typically sharper in individual brain images than in mean anatomical templates • But, we want to eliminate bias from registration to one individual • Statistics are performed on deformation tensors So... compute the average (using deformation tensors) after the registration!

  26. Averaging procedure . . . data templates common space The new deformation tensors are the (Log-Euclidean) average of the deformation tensors at each voxel in the common space. Sum over voxels to get a distance between brains.

  27. Anatomical correlations in twins p-values Identical twins Fraternal twins Significance of the Intraclass Correlation (ICC)

  28. Template centering Distance Number of Templates Distance to all the brains in the dataset using 1 to 9 templates

  29. TBM for DTI (Lee et al., MICCAI 2008) We can use almost the same procedure for DTI data!

  30. MRI vs. DTI Registration: DTI data is harder to register, so register the MRI and apply the deformation to the DTI 2. The DTI tensors will be misaligned by the registration, so tensors need to be rotated Statistics: Perform statistics on the diffusion tensors instead of the deformation tensors

  31. NYCAP algorithm team Principal Investigator: Paul Thompson Graduate students: Agatha Lee Caroline Brun External Collaborator: Xavier Pennec, INRIA Research Assistants: Yi-Yu Chou Marina Barysheva

More Related