Intro to Free Surfer Jargon Intro to Free
- Slides: 25
Intro to Free. Surfer Jargon
Intro to Free. Surfer Jargon voxel surface volume vertex surface-based recon cortical, subcortical parcellation/segmentation registration, morph, deform, transforms (computing vs. resampling)
Intro to Free. Surfer Jargon voxel
Intro to Free. Surfer Jargon surface
Intro to Free. Surfer Jargon surface
Intro to Free. Surfer Jargon vertex
Recon “recon your data” …short for reconstruction …cortical surface reconstruction …shows up in command recon-all
Recon
Volumes orig. mgz T 1. mgz brainmask. mgz wm. mgz filled. mgz (Subcortical Mass)
Cortical vs. Subcortical GM cortical gm subcortical gm sagittal coronal
Cortical vs. Subcortical GM subcortical gm sagittal coronal
Parcellation vs. Segmentation (cortical) parcellation (subcortical) segmentation
Intro to Free. Surfer Jargon voxel surface volume vertex surface-based recon cortical, subcortical parcellation/segmentation registration, morph, deform, transforms (computing vs. resampling)
Free. Surfer Questions Search for terms and answers to all your questions in the Glossary, FAQ, or Free. Surfer Mailing List Archives
What Free. Surfer Does… Free. Surfer creates computerized models of the brain from MRI data. Input: T 1 -weighted (MPRAGE) 1 mm 3 resolution (. dcm) Output: Segmented & parcellated conformed volume (. mgz)
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
Inter-subject, uni-modal example target Flirt 612/13/2011 DOF subject Flirt 9 DOF Flirt 12 DOF
Linear registration: 6, 9, 12 DOF target subject Flirt 12 DOF 9 DOF 6
Linear registration: 6, 9, 12 DOF Flirt 6 dof 9 dof target subject Flirt 12 DOF
Linear registration: 6, 9, 12 DOF Flirt 6 DOF 9 target subject Flirt 12 DOF
Intra-subject, multi-modal example before spatial alignment after spatial alignment
before spatial alignment after spatial alignment
before spatial alignment after spatial alignment
Inter-subject non-linear example target CVS reg
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
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