FreeSurfer Why use FreeSurfer? What happens? How do I do ... · • FreeSurfer can read/write:...
Transcript of FreeSurfer Why use FreeSurfer? What happens? How do I do ... · • FreeSurfer can read/write:...
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FreeSurferhttp://surfer.nmr.mgh.harvard.edu
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Platforms: Linux and Mac
Why use FreeSurfer?What happens?
How do I do that?Now What?
Surface and Volume Analysis
Cortical Reconstructionand Automatic Labeling
Inflation and FunctionalMapping
Surface FlatteningSurface-based IntersubjectAlignment and Statistics
Automatic SubcorticalGray Matter Labeling
Automatic Gyral White Matter Labeling
Cortical Surface ReconstructionFreeSurfer creates computerized
models of the brain from MRI data.
Input:T1-weighted (MPRAGE,SPGR)
1mm3 resolution(.dcm)
Output:Segmented & parcellated conformed
volume(.mgz)
MGZ File Format
001.mgz• mgz = compressed MGH file• Can store 4D (like NIFTI)• cols, rows, slices, frames• Generic: volumes and surfaces
• Eg, Typical Anatomical volume: 256 x 256 x 128 x 1
• FreeSurfer can read/write:NIFTI, Analyze, MINC Careful with NIFTI! (has 32k limit; surfaces could be more)
• FreeSurfer can read:DICOM, Siemens
IMA, AFNI
How to Get Started
Use dicoms from scanner as input to Cortical Reconstruction command:
recon-all -all -s <subject>
must do for each subject.
Come back in 30 hours …Check your results – accurate to the tissue
boundaries?
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Output of Cortical Surface Reconstruction What Happens During Cortical Surface Reconstruction?
• Finds wm/gray boundary – white surface• Finds pial/CSF boundary – pial surface• Subcortical Segmentation• Cortical Parcellation• Generates surface-based cross-subject
registration
What Happens During Cortical Surface Reconstruction?
• Finds wm/gray boundary – white surface
one sliceentire brain
What Happens During Cortical Surface Reconstruction?
• Finds pial/CSF boundary – pial surface
one sliceentire brain
Surface Model
• Mesh of triangles gives a measurable size
• Allows us to measure Area, Curv., Thickness (distance b/w vertices)
• Vertex = point of 6 triangles• Triangles/Faces ~ 150,000 per hemi• 1:1 correspondence of vertices• XYZ at each vertex
Cortical Thickness-autorecon2-finalsurfs
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What Happens During Cortical Surface Reconstruction?
• Subcortical Segmentation
Caudate
Pallidum
Putamen
AmygdalaHippocampus
Lateral Ventricle
Thalamus
White MatterCortex
What Happens During Cortical Surface Reconstruction?
• Cortical ParcellationPrecentral Gyrus Postcentral Gyrus
Superior Temporal Gyrus
What Happens During Cortical Surface Reconstruction?
• Finds white/gray boundary – wm surface• Finds pial/CSF boundary – pial surface• Subcortical Segmentation• Cortical Parcellation• Generates surface-based cross-subject
registration
Why FreeSurfer• Why Surface-based Analysis?
– Function has surface-based organization– Visualization: Inflation/Flattening– Cortical Morphometric Measures– Inter-subject registration
• Automatically generated ROI tuned to each subject individually
Use FreeSurfer Be Happy
Why Is a Model of the Cortical Surface Useful?
Local functional organization of cortex is largely 2-dimensional! Eg, functional mapping of primary visual areas:
From (Sereno et al, 1995, Science).
Even if you don’t care about the anatomy, anatomical models allow functional analysis not otherwise possible.
*Also, smooth along surface
Why use FreeSurfer?What happens?
How do I do that?Now What?
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Individual StepsVolumetric Processing Stages (subjid/mri):1. Motion Cor, Avg, Conform (orig.mgz)2. Non-uniform inorm (nu.mgz)3. Talairach transform computation
(talairach/talairach.xfm)4. Intensity Normalization 1 (T1.mgz)5. Skull Strip (brainmask.mgz)
6. EM Register (linear volumetric registration)7. CA Intensity Normalization (norm.mgz)8. CA Non-linear Volumetric Registration 9. CA Label (Volumetric Labeling) (aseg.mgz)
10. Intensity Normalization 2 (T1.mgz)11. White matter segmentation (wm.mgz)12. Edit WM With ASeg13. Fill and cut (filled.mgz)
Surface Processing Stages (subjid/surf):14. Tessellate (?h.orig.nofix)15. Smooth1 16. Inflate117. QSphere (?h.qsqhere)18. Automatic Topology Fixer (?h.orig)19. Final Surfs (?h.white ?h.pial ?.thickness)20. Smooth2 (?h.smoothwm)21. Inflate2 (?h.inflated)22. Aseg Statistics (stats/aseg.stats)23. Cortical Ribbon Mask (?h.ribbon.mgz)
24. Spherical Morph25. Spherical Registration (?h.sphere.reg)26. Map average curvature to subject27. Cortical Parcellation (Labeling) 28. Cortical Parcellation Statistics29. Cortical Parcellation mapped to Aseg30. White Matter Parcellation (wmparc.mgz)
recon-all -help Note: ?h.orig means lh.orig or rh.orig
Blue = Manual Intervention
Reconstruction Stages
recon-all is broken into three stages– autorecon1– autorecon2– autorecon3
these 3 stages are equivalent to -all
-autorecon1Volumetric Processing Stages (subjid/mri):1. Motion Cor, Avg, Conform (orig.mgz)2. Non-uniform inorm (nu.mgz)3. Talairach transform computation
(talairach/talairach.xfm)4. Intensity Normalization 1 (T1.mgz)5. Skull Strip (brainmask.mgz)
6. EM Register (linear volumetric registration)7. CA Intensity Normalization (norm.mgz)8. CA Non-linear Volumetric Registration 9. CA Label (Volumetric Labeling) (aseg.mgz)
10. Intensity Normalization 2 (T1.mgz)11. White matter segmentation (wm.mgz)12. Edit WM With ASeg13. Fill and cut (filled.mgz)
Surface Processing Stages (subjid/surf):14. Tessellate (?h.orig.nofix)15. Smooth1 16. Inflate117. QSphere (?h.qsqhere)18. Automatic Topology Fixer (?h.orig)19. Final Surfs (?h.white ?h.pial ?.thickness)20. Smooth2 (?h.smoothwm)21. Inflate2 (?h.inflated)22. Aseg Statistics (stats/aseg.stats)23. Cortical Ribbon Mask (?h.ribbon.mgz)
24. Spherical Morph25. Spherical Registration (?h.sphere.reg)26. Map average curvature to subject27. Cortical Parcellation (Labeling) 28. Cortical Parcellation Statistics29. Cortical Parcellation mapped to Aseg30. White Matter Parcellation (wmparc.mgz)
recon-all -help
Motion Correction and Averaging
001.mgz
002.mgz
+ rawavg.mgz
orig
001.mgz 002.mgz
mri
rawavg.mgzDoes not change native resolution.
-autorecon1-motioncor
mri_motion_correct.fsl
Conform
Changes to 256^3, 1mm^3All volumes will be conformed.
rawavg.mgz orig.mgz
orig Volume
-autorecon1-motioncor
mri_convert -conform
Non-Uniform Intensity Correction
• Uses MNI tool• Removes B1 bias field
-nuintensitycor -autorecon1
nu Volumemri_nu_correct.mni
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Talairach Transform
• Computes 12 DOF transform matrix• Does NOT resample• MNI305 template• Used to report Talairach coords in papers• mri/transforms/talairach.xfm
mri
transforms
talairach.xfm
-autorecon1-talairach
talairach_avi
Intensity Normalization
• Presegmentation (T1.mgz)– All WM = 110 intensity– Pre- and Post-Skull
Strip
T1 Volume
-autorecon1-normalization
mri_normalize
Skull Strip
• Removes all non-brain– Skull, Eyes, Neck, Dura
• brainmask.mgz
Orig Volume Brainmask Volume
-autorecon1-skullstrip
mri_watershed
FreeSurfer Directory Tree
Subject ID
Each data set has its own unique SubjectId (eg, bert)
bert
bem stats src mri scripts surf tmp label trash
orig T1 brainmask wm aseg aparc+aseg wmparc
-autorecon2Volumetric Processing Stages (subjid/mri):1. Motion Cor, Avg, Conform (orig.mgz)2. Non-uniform inorm (nu.mgz)3. Talairach transform computation
(talairach/talairach.xfm)4. Intensity Normalization 1 (T1.mgz)5. Skull Strip (brainmask.mgz)
6. EM Register (linear volumetric registration)7. CA Intensity Normalization (norm.mgz)8. CA Non-linear Volumetric Registration 9. CA Label (Volumetric Labeling) (aseg.mgz)
10. Intensity Normalization 2 (T1.mgz)11. White matter segmentation (wm.mgz)12. Edit WM With ASeg13. Fill and cut (filled.mgz)
Surface Processing Stages (subjid/surf):14. Tessellate (?h.orig.nofix)15. Smooth1 16. Inflate117. QSphere (?h.qsqhere)18. Automatic Topology Fixer (?h.orig)19. Final Surfs (?h.white ?h.pial ?.thickness)20. Smooth2 (?h.smoothwm)21. Inflate2 (?h.inflated)22. Aseg Statistics (stats/aseg.stats)23. Cortical Ribbon Mask (?h.ribbon.mgz)
24. Spherical Morph25. Spherical Registration (?h.sphere.reg)26. Map average curvature to subject27. Cortical Parcellation (Labeling) 28. Cortical Parcellation Statistics29. Cortical Parcellation mapped to Aseg30. White Matter Parcellation (wmparc.mgz)
recon-all -help Note: lh processed completely first, then rh.
Automatic Volume Labeling
• Label subcortical structures and wm/gm
•Determine volumes of subcortical structures
•Used to fill in subcortical structures for later steps
ASeg Volume
Atlas: RB_all_2007-08-08
-autorecon2-subcortseg
steps 6-9, 22
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Volume-based Labeling
0 20 40 60 80 100 1200
2
4
6
8
10
intensity
% v
oxe
ls in
lab
el
WMGMlVThCaPuPaHpAm
Labeling is determined by location and intensity.
-autorecon2-subcortseg
ICBM Atlas
Why not just register to an ROI Atlas?
12 DOF(Affine)
Subject 1Subject 2 aligned with Subject 1
(Subject 1’s Surface)
Problems with Affine (12 DOF) Registration
Markov Random Field: Motivation
What is the probability that cortical gray matter occurs inferior to hippocampus?
0 20 40 60 80 100 1200
2
4
6
8
10
intensity
% v
oxe
ls in
lab
el
WMGMlVThCaPuPaHpAm
Can’t segment on intensity alone
ICBM Atlas
12 DOF(Affine)
Why not just register to an ROI Atlas?
Problems with Affine (12 DOF) Registration• ROIs need to be individualized.
Why we use atlas + intensity + spatial location + geometric info + neighboring voxels + other info…
Markov Random Field: Motivation
What is the probability that cortical gray matter occurs inferior to hippocampus?
Segmentation: MRF
Preliminary Segmentation
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Segmentation: MRF
Final Segmentation
White Matter Segmentation• Separates white matter from everything else• “Fills in” subcortical structures• Cerebellum removed, brain stem still there
wm Volume
-autorecon2-segmentation
mri_segment
mri_edit_wm_with_aseg
mri_pretess
Fill and Cut (Subcortical Mass)• Fills in any voids• Removes any islands• Removes brain stem• Separates hemispheres (each hemi has different value) • filled.mgz = “Subcortical Mass”
WM Volume Filled Volume
-autorecon2-fill
mri_fill
FreeSurfer Directory Tree
Subject ID
Each data set has its own unique SubjectId (eg, bert)
bert
bem stats src mri scripts surf tmp label trash
orig T1 brainmask wm aseg aparc+aseg wmparc
Tessellation
• Mosaic of triangles (“tessellation”)• Errors: Donut holes, handles
- Subsequently fixed by the automatic topology fixer
orig surface surf/lh.origsurf/rh.orig
-autorecon2-tessellation
Inflation: Visualization
Dale and Sereno, 1993; Dale et al., Dale et al., 1999; Fischl et al., 1999; Fischl et al., 2000; Fischl et al., 2001
Gyri
Sulci
-autorecon2-inflate
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Automatic Topology Fixer
Fornix
Pallidum and Putamen
hippocampus
Ventricles and Caudate
Cortical Defects
• Holes• Handles• Automatically Fixed
optic nerve
-autorecon2-fix
Manifold Surgery
BEFORE AFTER
White Matter Surface• Nudge orig surface• Follow T1 intensity gradients• Smoothness constraint• Vertex Identity stays constant
-autorecon2-finalsurfs
Pial Surface
• Nudge white surface• Follow T1 intensity gradients• Vertex Identity Stays
-autorecon2-finalsurfs
Optimal Surface Placement
Gray-White Boundary
Outer Cortical Surface
-autorecon2-finalsurfs
Cortical Thickness
white/gray surface
pial surface
lh.thickness, rh.thickness
• Distance between white and pial surfaces
• One value per vertex• Surface-based more
accurate than volume-based
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Thickness Maps• Red regions are thinner• Yellow regions are thicker
Curvature (Radial)• Circle tangent to surface
at each vertex• Curvature measure is
1/radius of circle• One value per vertex• Signed (sulcus/gyrus)• Actually use gaussian
curvature
lh.curv, rh.curv
FreeSurfer Directory Tree
Subject ID
Each data set has its own unique SubjectId (eg, bert)
bert
bem stats src mri scripts surf tmp label
lh.white lh.curv lh.thickness lh.pial
-autorecon3Volumetric Processing Stages (subjid/mri):1. Motion Cor, Avg, Conform (orig.mgz)2. Non-uniform inorm (nu.mgz)3. Talairach transform computation
(talairach/talairach.xfm)4. Intensity Normalization 1 (T1.mgz)5. Skull Strip (brainmask.mgz)
6. EM Register (linear volumetric registration)7. CA Intensity Normalization (norm.mgz)8. CA Non-linear Volumetric Registration 9. CA Label (Volumetric Labeling) (aseg.mgz)
10. Intensity Normalization 2 (T1.mgz)11. White matter segmentation (wm.mgz)12. Edit WM With ASeg13. Fill and cut (filled.mgz)
Surface Processing Stages (subjid/surf):14. Tessellate (?h.orig.nofix)15. Smooth1 16. Inflate117. QSphere (?h.qsqhere)18. Automatic Topology Fixer (?h.orig)19. Final Surfs (?h.white ?h.pial ?.thickness)20. Smooth2 (?h.smoothwm)21. Inflate2 (?h.inflated)22. Aseg Statistics (stats/aseg.stats)23. Cortical Ribbon Mask (?h.ribbon.mgz)
24. Spherical Morph25. Spherical Registration (?h.sphere.reg)26. Map average curvature to subject27. Cortical Parcellation (Labeling) 28. Cortical Parcellation Statistics29. Cortical Parcellation mapped to Aseg30. White Matter Parcellation (wmparc.mgz)
recon-all -help Note: lh processed completely first, then rh.
Surface-Based Spherical Coord System “Spherical” Registration
Spherical Inflation
High-DimensionalRegistration toSpherical Template
Template: average.curvature.filled.buckner.40.tiff
Inflated Surface (Sulcal Map)
Atlas (Target)
Individual Subject
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Spherical InflationRegistration to Atlas
-autorecon3-sphere-surfreg
Atlas (Target)
Individual Subject
Cortical Parcellation
Fine-tune based onindividual anatomy
Spherical Template based on Manual Parcellation
Map to IndividualThru Spherical Reg
Atlases: curvature.buckner40filled.desikan_killiany, atlas_2005_simple
-autorecon3-cortparc
Non-Cortical Areas of Surface
Amygdala, Putamen, Hippocampus, Caudate, Ventricles, CC
Amygdala
?h.cortex.label
The “Unknown” Label
Cortical Parcellation: 2
AutomaticManual
Atlases: curvature.buckner40filled.desikan_killiany, atlas_2005_simple
Thanks to Christophe Destrieux for this slide.
-cortparc2 -autorecon3
Aparc+Aseg
There is also aparc.a2005s+aseg.mgz for Destrieux atlas
-aparc2aseg -autorecon3
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White Matter Parcellation
Salat DH, Greve DN, Pacheco JL, Quinn BT, Helmer KG, Buckner RL,Fischl B. Regional white matter volume differences in nondemented aging and Alzheimer’s disease. Neuroimage. 2008 Nov 5.
-wmparc -autorecon3
Nearest Cortical Labelto point in White Matter
FreeSurfer Directory Tree
Subject ID
Each data set has its own unique SubjectId (eg, bert)
bert
bem stats src mri scripts surf tmp label
lh.aparc.annot rh.aparc.annot
FreeSurfer Directory Tree
Subject ID
Each data set has its own unique SubjectId (eg, bert)
bert
bem stats src mri scripts surf tmp label
orig T1 brainmask wm aseg aparc+aseg wmparc
FreeSurfer Directory Tree
Subject ID
Directories used often are in green.
bert
bem stats src mri scripts surf tmp label trash
aseg.stats lh.aparc.stats rh.aparc.stats wmparc.stats
Why use FreeSurfer?What happens?
How do I do that?Now What?
Starting the Reconstruction Process
recon-all \-i /path/to/your/raw/data1 \-i /path/to/your/raw/data2 \-all -s subject_id
• This will create the subject directory ‘subject_id’ in your $SUBJECTS_DIR and convert your 2 raw acquisitions to mgz and use them as input for the ‘-all’ command.
Before running FreeSurfer, must set $FREESURFER_HOME and $SUBJECTS_DIR
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Set-up Environmental Variables
bert
$SUBJECTS_DIR
fred sara margaret …
Subject ID
Before running FreeSurfer, must set $FREESURFER_HOME and
$SUBJECTS_DIR
FreeSurfer Directory Tree
Subject ID
Each data set has its own unique SubjectId (eg, bert)
bert
bem stats src mri scripts surf tmp label trash touch
Alternative: Add Your Data• cd $SUBJECTS_DIR• mkdir –p bert/mri/orig• mri_convert yourdicom.dcm bert/mri/orig/001.mgz• mri_convert yourdicom.dcm bert/mri/orig/002.mgz• recon-all –all –s bert
bert
bem label src mri scripts surf tmp label
orig001.mgz 002.mgz
FreeSurfer Output
• Volumes• Surfaces• Surface Overlays• ROI Summaries
Volumes
orig.mgz
• $SUBJECTS_DIR/bert/mri• All “Conformed” 256^3, 1mm• File format: .mgz
aseg.mgz
T1.mgz brainmask.mgz wm.mgz filled.mgzSubcortical Mass
aparc+aseg.mgzVolume Viewer:tkmedit
Surfaces
.orig .white .pial
.inflated sphere, sphere.reg flat• $SUBJECTS_DIR/bert/surf•Number/Identity of vertices stays the same (except flat)•XYZ Location Changes•Flattening not done as part of standard reconstruction
Surface Viewer:tksurfer
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Surface Overlays
• Value for each vertex• Color indicates value• Color: gray,red/green, heat, color table• Rendered on any surface• fMRI/Stat Maps too
lh.sulc on inflated lh.curv on inflated lh.thickness on inflated
lh.sulc on pial
lh.aparc.annot on inflated
lh.curv on inflated fMRI on flat
Other FreeSurfer File FormatsUnique to FreeSurfer
•Surface: lh.white, lh.pial, lh.orig
• Curv: lh.curv, lh.sulc, lh.thickness
• Annotation: lh.aparc.annot
• Label: lh.pericalcarine.label
ROI Summaries:$SUBJECTS_DIR/subjid/stats
aseg.stats – volume summaries?h.aparc.stats – desikan/killiany atlas summaries?h.aparc.a2005s.stats – destrieux atlas summarieswmparc.stats – volume summaries
_______________________________________________aseg:• volumes of subcortical structures (mm3)
aparc:• thickness of cortical parcellation structures (mm)• total white matter volume (mm3) • number of vertices in cortex• surface area of cortex (mm2)
Aseg Stats FileIndex SegId NVoxels Volume_mm3 StructName Mean StdDev Min Max Range
1 2 255076 255076.0 Left-Cerebral-White-Matter 101.5872 7.9167 34.0000 148.0000 114.0000 2 3 266265 266265.0 Left-Cerebral-Cortex 75.3682 9.4016 28.0000 152.0000 124.0000 3 4 5855 5855.0 Left-Lateral-Ventricle 37.7920 10.9705 20.0000 88.0000 68.0000 4 5 245 245.0 Left-Inf-Lat-Vent 56.4091 9.5906 26.0000 79.0000 53.0000 5 7 16357 16357.0 Left-Cerebellum-White-Matter 91.2850 4.8989 49.0000 106.0000 57.0000 6 8 60367 60367.0 Left-Cerebellum-Cortex 76.3620 9.5724 26.0000 135.0000 109.0000 7 10 7460 7460.0 Left-Thalamus-Proper 91.3778 7.4668 43.0000 108.0000 65.0000 8 11 3133 3133.0 Left-Caudate 78.5801 8.2886 42.0000 107.0000 65.0000 9 12 5521 5521.0 Left-Putamen 86.9680 5.5752 66.0000 106.0000 40.0000
10 13 1816 1816.0 Left-Pallidum 97.7162 3.4302 79.0000 106.0000 27.0000 11 14 852 852.0 3rd-Ventricle 41.9007 11.8230 22.0000 69.0000 47.0000 12 15 1820 1820.0 4th-Ventricle 39.7053 10.6407 20.0000 76.0000 56.0000 13 16 25647 25647.0 Brain-Stem 85.2103 8.2819 38.0000 106.0000 68.0000 14 17 4467 4467.0 Left-Hippocampus 77.6346 7.5845 45.0000 107.0000 62.0000 15 18 1668 1668.0 Left-Amygdala 74.5104 5.8320 50.0000 94.0000 44.0000 16 24 1595 1595.0 CSF 52.1348 11.6113 29.0000 87.0000 58.0000
Index: nth Segmentation in stats fileSegId: index into lookup tableNVoxels: number of Voxels/Vertices in segmentationStructName: Name of structure from LUTMean/StdDev/Min/Max/Range: intensity across ROI
Eg: aseg.stats, wmparc.statscreated by mri_segstats
Getting Stats into Table Format
asegstats2table \ Command name--subjects 001 002 003 \ List subjects to include--meas vol \ vol or mean intensity of struct--t asegstats.txt text output file
asegstats.txt created in SUBJECTS_DIR
Using Excel: File > Open Select asegstats.txtChoose space as delimiter
Parcellation Stats File
StructName: Name of structure/ROINumVert: number of vertices in ROISurfArea: Surface area in mm2GrayVol: volume of gray matter ThickAvg/ThickStd – average and stddev of thickness in ROIMeanCurv – mean Gaussian curvatureGausCurv – Gaussian curvatureFoldInd – folding indexCurvInd – curvature index
Eg, lh.aparc.stats, lh.a2005s.aparc.statscreated by mris_anatomical_stats
StructName NumVert SurfArea GrayVol ThickAvg ThickStd MeanCurv GausCurv FoldInd CurvIndunknown 10863 7151 13207 1.776 1.629 0.121 0.107 383 50.8bankssts 1222 830 2290 2.711 0.559 0.112 0.027 10 1.3caudalanteriorcingulate 830 585 1459 2.474 0.569 0.128 0.020 10 0.7caudalmiddlefrontal 2509 1658 4979 2.653 0.567 0.125 0.035 27 3.5corpuscallosum 2124 1340 569 0.489 0.631 0.151 0.110 87 8.0cuneus 2737 1706 3086 1.741 0.509 0.162 0.065 52 8.0entorhinal 495 330 1685 3.150 0.753 0.149 0.187 15 1.5fusiform 3878 2638 7887 2.627 0.724 0.137 0.046 57 6.7
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Getting Stats into Table Format
aparcstats2table \ Command name--subjects 001 002 003 \ List subjects to include--h rh \ Which hemisphere--meas volume \ volume, thickness, or area--t rh.aparc.vol.txt text output file
rh.aparc.vol.txt created in SUBJECTS_DIR
Using Excel: File > Open Select asegstats.txtChoose space as delimiter
Other Info in aseg.stats• Brain – gray matter + white matter + ventricles +
hypointensities + hyperintensities• Total Gray – cortical + subcortical gray• Cortical Gray – volume between white and pial
excluding non-cortical regions.• Intracranial volume (ICV), estimated Total
Intracranial Volume (eTIV)
• Total Number of Vertices• Total Surface Area
Other Info in aparc.stats
ROI Summaries: Make Your Own
Draw your own surface label in region of interest.
Run:mris_anatomical_stats –l your_roi.label
to gets stats on that label
Some other relevant commands
• recon-all –make all –s <subjid>– reruns stream from most outdated step
• recon-all –all –clean –s <subjid>– reruns subject without using existing edits
• recon-all –all –legacy– use on data processed w/ previous version of
FS to keep prior edits• recon-all –<arg> –dontrun
– prints command to screen without executing ithttp://surfer.nmr.mgh.harvard.edu/fswiki/OtherUsefulFlags
Troubleshooting
• check recon-all.log• try to rerun step that failed • look at volume from last successful step• examine data quality to see what might cause error• if it fails again, attempt to run modified version of command
if possible• search FreeSurfer mailing list for other instances of this
problem:http://www.mail-archive.com/[email protected]/• email the mailing list if still need help
recon-all fails
FreeSurfer Directory Tree
Subject ID
Each data set has its own unique SubjectId (eg, bert)
bert
bem stats src mri scripts surf tmp label
recon-all.log recon-all-status.log
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Bug Reporting
• Report version currently using – see top of recon-all.log– more $FREESURFER_HOME/build-stamp.txt
• command line tried to run• attach recon-all.log• Output in terminal window if appropriate• Operating System
Why use FreeSurfer?What happens?
How do I do that?Now What?
Troubleshooting
• Skull Strip Errors• Intensity Normalization• Segmentation Errors• Topology Fixer Errors• Pial Surface• Talairach Errors
recon-all was successful but errors in volumes
MR Anatomy CaveatsSurfaces are only as good as your scan.
• Dependent on data quality– Contrast to noise– Signal to noise– Voxel resolution
• MR Artifacts– MR susceptibility– MR distortions
• Variations in MR tissue parameters across regions of the brain are altered in different populations
Cortical Reconstruction Goals
• Geometrically Accurate surfaces– Accurately follow the boundaries seen on the
scan for each of your individual subjects• Topologically Correct surfaces
– Each surface is a 2-D continuous, non self-intersecting sheet and can be inflated into a perfect sphere
Troubleshooting: Skull Strip
brainmask.mgz
–wsthresh 25 (increase to strip less; decrease to strip more)
– multistrip orig.mgz (automatically get different flood heights)
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Rerun Skullstrip
1. recon-all –all (Stages 1-30) ~30 hoursCheck talairach transform, skull strip, normalization Check surfaces1. Add control points: recon-all –autorecon2-cp –autorecon3
(Stages 10-30)2. Edit wm.mgz: recon-all –autorecon2-wm –autorecon3
(Stages 13-30)3. Edit brainmask.mgz: recon-all –autorecon2-pial
-autorecon3 (Stage 19-30)Note: all stages can be run individually
Intensity Normalization Failure. All WM in T1 volume (T1.mgz) should be 110.Can fix by adding “Control Points”. Beware partial voluming!
Troubleshooting: Intensity Normalization
Rerun Normalization
1. recon-all –autorecon1 (Stages 1-5) ~45 minCheck talairach transform, skull strip, normalization
2. recon-all –autorecon2 (Stages 6-23) ~20 hoursCheck surfaces
1. Add control points: recon-all –autorecon2-cp (Stages 10-23)
2. Edit wm.mgz: recon-all –autorecon2-wm (Stages 13-23)3. Edit brainmask.mgz: recon-all –autorecon2-pial (Stage
19-23)3. recon-all –autorecon3 (Stages 24-30) ~6 hours
Note: all stages can be run individually
Processing Stream Order
Eye Socket classified as WM.Skull Strip Failure.
Troubleshooting: Segmentation Error
“Hypo-Intensities”White Matter Lesions
Troubleshooting: Segmentation Error
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White Matter “disconnects”
Troubleshooting: Topology Fixer Error
Fill in gap or “hole”manually
White Matter “disconnects”
orig.nofix will be accurate
Troubleshooting: Topology Fixer Error
Rerun WM Segmentation
1. recon-all –autorecon1 (Stages 1-5) ~45 minCheck talairach transform, skull strip, normalization
2. recon-all –autorecon2 (Stages 6-23) ~20 hoursCheck surfaces
1. Add control points: recon-all –autorecon2-cp (Stages 10-23)
2. Edit wm.mgz: recon-all –autorecon2-wm (Stages 13-23)3. Edit brainmask.mgz: recon-all –autorecon2-pial (Stage
19-23)3. recon-all –autorecon3 (Stages 24-30) ~6 hours
Note: all stages can be run individually
White/Gray OK, butPial Inaccurate
Troubleshooting: Pial Surface Error
Rerun Final Surfaces
1. recon-all –autorecon1 (Stages 1-5) ~45 minCheck talairach transform, skull strip, normalization
2. recon-all –autorecon2 (Stages 6-23) ~20 hoursCheck surfaces
1. Add control points: recon-all –autorecon2-cp (Stages 10-23)
2. Edit wm.mgz: recon-all –autorecon2-wm (Stages 13-23)3. Edit brainmask.mgz: recon-all –autorecon2-pial (Stage
19-23)3. recon-all –autorecon3 (Stages 24-30) ~6 hours
Note: all stages can be run individually
Troubleshooting
• check recon-all.log• try to rerun step that failed • look at volume from last successful step• examine data quality to see what might cause error• if it fails again, attempt to run modified version of command
if possible• search FreeSurfer mailing list for other instances of this
problem:http://www.mail-archive.com/[email protected]/• email the mailing list if still need help
recon-all fails
18
FreeSurfer Directory Tree
Subject ID
Each data set has its own unique SubjectId (eg, bert)
bert
bem stats src mri scripts surf tmp label
recon-all.log recon-all-status.log
Bug Reporting
• Report version currently using – see top of recon-all.log– more $FREESURFER_HOME/build-stamp.txt
• command line tried to run• attach recon-all.log• Output in terminal window if appropriate• Operating System