Intro to FreeSurfer Jargon

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Presentation transcript:

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

What FreeSurfer Does… FreeSurfer creates computerized models of the brain from MRI data. Input: T1-weighted (MPRAGE) 1mm3 resolution (.dcm) Output: Segmented & parcellated conformed volume (.mgz)

Intro to FreeSurfer Jargon voxel

Intro to FreeSurfer Jargon surface

Intro to FreeSurfer Jargon surface

Intro to FreeSurfer Jargon vertex

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

Recon

Volumes orig.mgz T1.mgz brainmask.mgz wm.mgz filled.mgz (Subcortical Mass)

Cortical vs. Subcortical GM sagittal coronal

Cortical vs. Subcortical GM sagittal coronal

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

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

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

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 12/13/2011

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

Linear registration: 6, 9, 12 DOF subject target Flirt 12 DOF Flirt 9 DOF Flirt 6 DOF 12/13/2011 17

Linear registration: 6, 9, 12 DOF Flirt 9dof Flirt 12 DOF Flirt 6dof subject target 12/13/2011

Linear registration: 6, 9, 12 DOF Flirt 9 DOF Flirt 12 DOF Flirt 6 DOF subject target 12/13/2011

Intra-subject, multi-modal example before spatial alignment 12/13/2011 after spatial alignment

before spatial alignment after spatial alignment 12/13/2011

before spatial alignment after spatial alignment 12/13/2011

Inter-subject non-linear example target CVS reg 12/13/2011

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 12/13/2011