Lilla Zöllei A.A. Martinos Center, MGH; Boston, MA

Transcription

1 Lilla Zöllei A.A. Martinos Center, MGH; Boston, MA

2 Bruce Fischl Gheorghe Postelnicu Jean Augustinack Anastasia Yendiki Allison Stevens Kristen Huber Sita Kakonoori + the FreeSurfer team KEPAF

3 Pre-processing step Surgical planning, radiation treatment, Intra- and inter-subject comparison Multi-modality fusion / comparison / validation Longitudinal analysis (development, aging, drug treatment) Atlas building Etc KEPAF

4 PDw MRI T2w MRI unknown ground truth transformation The goal is: KEPAF

5 Affine transform of surfaces from one subject mapped to another. KEPAF

6 Template Affine Nonlinear pial surface WM surface 6

7 Local functional organization of cortex is largely 2-dimensional! (Sereno et al, 1995, Science). 7

8 D.J. Felleman and D.C. Van Essen, CC, 1991 KEPAF

9 ultimate goal: pial (GM CSF) surface BUT: impossible to directly generate representation from MRI (many have tried!) alternative: construct interim representation of the interface between GM and WM infer the location of the true cortical surface (Dale and Sereno, 1993) KEPAF

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11 Courtesy of B. Fischl 11

12 spherical registration for each of the hemispheres Template Source Sulcal pattern projected on the sphere Registered Source KEPAF

13 Motivation: Surface-based (2D) registration does an excellent job of aligning cortical folds, but does not apply to non-cortical structures (e.g. basal ganglia). Volumetric (3D) registration applies to the entire brain but does not in general align folding patterns. Goal: integrate them KEPAF

14 Our goal: automated and accurate registration of both cortical folding patterns and non-cortical regions in 3D Previous work: Peckar, JMIV99 Boundary mapping as a hard constraint for volumetric warp Ferrant TMI01 Volumetric deformation inferred after tracking key surface boundaries; brain shift modellin Collins, ICVBC96 combine the use of intensity measures with automatically extracted sulci Thompson, TMI96 ASM to extract the external cortical surface and the lateral ventricles; volumetric elastic model HAMMER, Shen TMI02 implicitly combines surface and volume information in elastic registration Liu, NeuroImage04 surface-driven registration after HAMMER Joshi, TMI07 surface-based alignment using manually selected sulcal landmarks and inverse-consistent volumetric registration KEPAF

15 Combined volume- and surface-based registration: Automated Surface-based Registration Initialization via an Elastic Transform Intensity-based Volumetric Registration KEPAF

16 accurate, topologically correct reconstructions of the cortical surfaces (L/R white/pial) registration in the spherical space 1-to-1 mapping J= J p + λ J + A A λ d J d Data term Topology preservation Metric distortion KEPAF

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18 to diffuse vector field from the cortical surfaces to the rest of the volume regularity constraint: elastic deformation, i.e. a smooth, orientationpreserving deformation satisfying our choice: Navier operator from the linear elasticity theory + finite element method (FEM) KEPAF

19 Iterate for i=1:n create mesh set up BC s from surface setup linear elastic system solve system solve topology problems Tetrahedral mesh used for one iteration of the elastic solver. Notice how the mesh is denser near the input surfaces KEPAF

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21 Template Affine HAMMER Elastic KEPAF

22 nonlinear intensity based registration [Fischl, NeuroImage04]: G = G I + λ G + T T λ S G S G I : intensity term G T : topology constraining term G S : smoothness term KEPAF

23 KEPAF

24 Template Elastic CVS Template KEPAF

25 mesh to be warped elastic CVS KEPAF

26 Compare: FLIRT, HAMMER, CVS Template: single, randomly selected scan; rest in the data set is registered to it Accuracy: Jaccard overlap metric KEPAF

27 Template HAMMER FLIRT CVS 27

28 Extended Jaccard Coefficient measures: 20 cortical and 21 sub-cortical labels. The vertical lines represent the standard error of the mean of the measurement.

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30 Average Jaccard Coefficient (SE: standard error) Cortical structures

31 Average Jaccard Coefficient (SE: standard error) Sub-cortical structures

32 Identify fiber bundles in cerebral white matter (WM) Characterizing these WM pathways is important for: Inferring connections b/w brain regions Understanding effects of neurodegenerative diseases, stroke, aging, development From Gray's Anatomy: IX. Neurology KEPAF Courtesy of A. Yendiki 32

33 Differentiate tissues based on the diffusion (random motion) of water molecules within them Gray matter: Diffusion is unrestricted isotropic White matter: Diffusion is restricted anisotropic KEPAF Courtesy of A. Yendiki 33

34 Magnetic resonance imaging can provide diffusion encoding Magnetic field strength is varied by gradients in different directions Image intensity is attenuated depending on water diffusion in each direction Compare with baseline images to infer on diffusion process No diffusion encoding KEPAF g 4 g 5 Diffusion encoding in direction g 1 g6 g 2 g 3 Courtesy of A. Yendiki

35 Trackvis and Diffusion Toolkit ( KEPAF

36 (a) (b) (c) (d) (a) deterministic tractography seeded in the whole brain (b) tracts going through the precentral gyri ROIS (c) tracts going through the brainstem ROI (d) tracts going through both the precentral gyri and brainstem ROIs. KEPAF

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40 KEPAF

41 Goal: fiber bundle alignment Study: compare CVS to methods directly aligning DWI-derived scalar volumes Registration methods: FLIRT FA-FLIRT FA-FNIRT CVS KEPAF

42 (a) FLIRT (b) FA-FLIRT (c) FA-FNIRT (d) CVS 42

43 (a) FLIRT (b) FA-FNIRT (c) CVS 43

44 (a) CST (b) ILF (c) UNCINATE 44

45 CVS outperformed FLIRT, FA-FLIRT and FA-FNIRT for all three tracts and both hemispheres, in a statistically significant manner (p-values were computed using the Student T-test with alpha<.0025) FA-FLIRT was outperformed by all other three methods in a statistically significant manner FLIRT was outperformed also by FA-FNIRT in all cases in a statistically significantly manner except for lh UNC and lh ILF where FLIRT outperformed FA-FNIRT Note: accuracy of the linear registration computed by FLIRT increased substantially when using the structural data (FLIRT) over the DWI data (FA-FLIRT), even though the gold standard we use for assessing accuracy is derived from the DWI data. KEPAF

46 We ran two sets of further experiments: after completing our CVS registration framework we added an additional registration step using FA information from the diffusion images. CVS+FLIRT-FA CVS+FNIRT-FA KEPAF

47 (a) CST (b) ILF (c) UNCINATE 47

48 high accuracy cross-subject registration based on structural MRI images can provide improved alignment L. Zöllei, A. Stevens, K. Huber, S. Kakunoori, B. Fischl: "Improved Tractography Alignment Using Combined Volumetric and Surface Registration", NeuroImage 51 (2010), KEPAF

49 Different image acquisition sequences In-vivo MRI scans: T1-weighted magnetization-prepared rapid gradient echo (MP-RAGE) scans were obtained according to the following protocol: two sagittal acquisitions, FOV = 224, Matrix = 256x256, Resolution = 1x1x1.25 mm, TR = 9.7 ms, TE = 4 ms, Flip angle = 10o, TI = 20 ms, TD = 200 ms. Two acquisitions were averaged together to increase the contrast-to-noise ratio Ex-vivo MRI scans: T2*-weighted Multi Echo Flash protocol due to the reduced T1 contrast observed post-mortem Mutual information (MI) replaces the volumetric registration objective function Lilla Zöllei, Allison Stevens, Bruce Fischl: Non-linear registration of intra-subject ex-vivo and in-vivo brain acquisitions ; HBM, 2010 Barcelona KEPAF

50 in vivo ex vivo Courtesy of Xiao Han. 50

51 Target (in-vivo) Masked target 2-step CVS CVS with MI 51

52 Target (in-vivo) Masked target 2-step CVS CVS with MI 52

53 Target (in-vivo) Masked target 2-step CVS CVS with MI 53

54 Target (in-vivo) Masked target 2-step CVS CVS with MI 54

55 Manual segmentation of ex-vivo MRI Qualitative validation Increase robustness to scanning artifacts KEPAF

56 KEPAF