Combined Volumetric and Surface (CVS) Registration FS workshop Gheorghe Postelnicu, Lilla Zöllei, Bruce Fischl A.A. Martinos Center for Biomedical Imaging, MGH

Surface-based (2D) registration does an excellent job of aligning cortical folds, but doesn’t apply to non-cortical structures (e.g. basal ganglia). Volumetric (3D) registration applies to the entire brain but doesn’t in general align folding patterns. Solution: integrate them Introduction and motivation

Why Aligning Folds in the Volume is Hard! Affine transform of surfaces from one subject mapped to another.

Template Affine Nonlinear pial surfaceWM surface

Outline Proposed Framework: –Automated Surface-based registration –Initialization via an Elastic Transform –Intensity-based Volumetric Registration Experiments Validation

accurate, topologically correct reconstructions of the cortical surfaces (L/R white/pial) registration in the spherical space  1-to-1 mapping Surface-based registration Metric distortion Topology preservation Data term

Reconstruction of cortical surfaces

Surface Morph spherical registration for each of the hemispheres independently Sulcal pattern projected on the sphere TemplateRegistered Source Source

Surface Morph

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

Elastic Morph 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

Example of Morph Dynamics

Results – Elastic Morph Template Affine HAMMER Elastic

Intensity-based registration Nonlinear optical flow intensity based registration [Fischl, NeuroImage04] G I : intensity term G T : topology constraining term G S : smoothness term

Resulting Morph Template Elastic CVS Template

Step 2: Elastic deformation

Step 3: Intensity deformation

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

Experiments 1.Adult brain inter-subject [R. Buckner]: –40 subjects (66 cortical+21subcortical manually segmented labels) 2.IBSR dataset, CMA, MGH ( –10 subjects –15 GM/WM structures; mostly sub-cortical –common with Joshi

The last column of the cortical measures represents the overlap measure computed in the surface space. The vertical lines represent the standard error of the mean of the measurement. 2

TEMPLATE

FLIRT

HAMMER

ELASTIC

CVS

TEMPLATE

FLIRT

HAMMER

ELASTIC

CVS

TEMPLATE

3 Overlap measure: JaccardIn figures: mean and standard error

Template 3

FLIRT 3

HAMMER 3

CVS 3

FLIRT HAMMER CVS 3 Template

CVS registration script mri_cvs_register --template $template --mov $subjid USAGE: mri_cvs_register Required Arguments: --mov subjid: subjid for subject to be moved / registered --template subjid: subjid for subject to be kept fixed (template)** Optional Arguments --outdir directory: output directory where all the results are written (default is SUBJECTS_DIR/mov/cvs) --noaseg: do not use aseg volumes in the registration pipeline (default is 0) --nocleanup: do not delete temporary files (default is 0) --nolog: do not produce a log file (default is 0) --version: print version and exit --help: print help and exit

Applications to tractography Goal: fiber bundle alignment Study: compare CVS to methods directly aligning DWI-derived scalar volumes Conclusion: high accuracy cross-subject registration based on structural MRI images can provide improved alignment Zöllei, Stevens, Huber, Kakunoori, Fischl: “Improved Tractography Alignment Using Combined Volumetric and Surface Registration”, NeuroImage 51 (2010),

Mean Hausdorff distance measures for three fiber bundles CSTILFUNCINATE

Average tracts after registration mapped to the template displayed with iso-surfaces FLIRTFA-FNIRTCVS

Average tracts after registration mapped to the template displayed with morphed tracks FLIRTFA-FNIRTCVS

Average tracts after registration mapped to the template displayed with morphed tracks FLIRTFA-FNIRTCVS

Ongoing development Improve CVS capability to register ex-vivo to in-vivo acquisitions Implemented MI-based volumetric registration (for CVS step 3) to accommodate intensity profile differences Qualitative preliminary results on 4 subjects L. Zöllei, Allison Stevens, Bruce Fischl: Non-linear Registration of Intra-subject Ex-vivo and In-vivo Brain Acquisitions, Human Brain Mapping, June 2010

Target (in-vivo) Masked target 2-step CVS CVS with MI Subject 1

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

Subject 3 Target (in-vivo) Masked target 2-step CVS CVS with MI

Subject 4 Target (in-vivo) Masked target 2-step CVS CVS with MI