1. Intro to FreeSurfer Jargon

2. voxel surface volume vertex surface-based recon cortical, subcortical parcellation/segmentation registration, morph, deform, transforms (computing vs. resampling)

3. Intro to FreeSurfer Jargon voxel

4. Intro to FreeSurfer Jargon surface

5. Intro to FreeSurfer Jargon surface

6. Intro to FreeSurfer Jargon vertex

7. What FreeSurfer Does… FreeSurfer creates computerized models of the brain from MRI data. Input: T1-weighted (MPRAGE) 1mm 3 resolution (.dcm) Output: Segmented & parcellated conformed volume (.mgz)

8. Recon “ recon your data” …short for reconstruction …cortical surface reconstruction … shows up in command recon-all

9. Recon

10. Volumes orig.mgzT1.mgzbrainmask.mgzwm.mgz filled.mgz (Subcortical Mass)

11. Cortical vs. Subcortical GM coronal sagittal subcortical gmcortical gm

12. Cortical vs. Subcortical GM coronal sagittal subcortical gm

13. Parcellation vs. Segmentation (subcortical) segmentation(cortical) parcellation

14. Intro to FreeSurfer Jargon voxel surface volume vertex surface-based recon cortical, subcortical parcellation/segmentation registration, morph, deform, transforms (computing vs. resampling)

15. Registration Goal: to find a common coordinate system for the input data sets Examples:  comparing different MRI images of the same individual (longitudinal scans, diffusion vs functional scans)  comparing MRI images of different individuals

16. 12/13/2011 Flirt 6 DOF subject Flirt 9 DOF target Inter-subject, uni-modal example Flirt 12 DOF

17. Linear registration: 6, 9, 12 DOF Flirt 6 DOF Flirt 9 DOFFlirt 12 DOF subject target

18. Linear registration: 6, 9, 12 DOF targetsubject Flirt 6dofFlirt 9dof Flirt 12 DOF

19. Linear registration: 6, 9, 12 DOF target subject Flirt 6 DOFFlirt 9 DOF Flirt 12 DOF

20. Intra-subject, multi-modal example before spatial alignment after spatial alignment

21. before spatial alignment after spatial alignment

22. before spatial alignment after spatial alignment

23. Inter-subject non-linear example targetCVS reg

24. Some registration vocabulary  Input datasets:  Fixed / template / target  Moving / subject  Transformation models  rigid  affine  nonlinear  Objective / similarity functions  Applying the results  deform, morph, resample, transform  Interpolation types  (tri)linear  nearest neighbor

25. FreeSurfer Questions Search for terms and answers to all your questions in the Glossary, FAQ, orGlossaryFAQ FreeSurfer Mailing List Archives