Outline Overview of CIVET and CLASP Identification of problems Recent improvements - brain-masking - non-uniformity corrections ...
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' Outline
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Overview of CIVET and CLASP
Identification of problems
Recent improvements
– brain-masking
– non-uniformity corrections
– registration
– surface extraction
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Work in progress and future work
Discussion
' Image-processing pipeline CIVET
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native t1, t2, pd
non−uniformity
corrections
linear
registration
transformation
to stx space
brain−masking
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CLASP
classification
pve
ANIMAL surface
segmentation extraction
' Observed problems
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Bad brain masking
– pieces of skull, meninges, eyes not masked out
– truncated temporal and occipital lobes
Failed registration
Under-expanded gray surfaces, bad folds between
hemispheres
Susceptibility artefacts
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Surface extraction is slow
To fix these problems, we must understand why they occur?
' Brain Masking - Skull and Eyes
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' Brain Masking – Truncated Mask
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occipital lobes temporal lobe
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The cortical thickness will be wrong in these regions.
The problem is due to failure of non-uniformity corrections.
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' Failed linear registration without masking
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transformed target combined
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The source image is transformed inside the target image!
Application of models (e.g. tag file for classification) will not be
optimal.
' Failed linear registration due to tall neck
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Failed non-linear registration
If linear registration is not good to start with or if brain mask
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contains “skull”
Non-linear registration is used to mask out brain stem,
cerebellum, sub-cortical gray, ventricles, (eyes)
' Under-expanded gray surface
old CLASP new CLASP
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Simply do more iterations and use oversampling!
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' Susceptibility artefacts
nasal cavities
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ear canals
blood vessels
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Artefacts can alter tissue classification locally.
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' How good is brain-masking?
mincbet now uses 20K surface instead of 5K surface
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Combination of t1, t2, pd images gives complementary
information for better delineation of csf and brain tissues
mincbet performs reliably if image has been properly corrected
for non-uniformities
t1 only t1, t2, pd
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cortical surface performs reliably if image has been properly
registered to stereotaxic space (uses a model mask)
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Application of a brain mask can help, but image must be
corrected in order to extract a reliable brain mask
Increase number of iterations, by cycles
Experiment with “distance” parameter for control points of the
spline in N3
– large distance (200mm) is fast to compute
– small distance (25mm) is more accurate but very slow
– distance must not be too small to interfere with brain
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structures
– hierarchical approach (200mm, 100mm, 75mm, 50mm,
25mm)?' Non-uniformity corrections for 3T scan
spline distance at 200mm suitable for 1.5T
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spline distance at 25mm looks interesting for 3T
before N3 after N3 (25mm)
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BrainVisa has a specific correction for 3T scans
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Correction of non-uniformities before registration
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Application of a brain mask on both source image and target is
critical (for both linear and non-linear)
– extents of image vary, but brain tissues are complete in all
scans (tall neck, clipped skull, etc)
without masking with masking
transformed target combined transformed target combined
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Cropping tall neck improves quality of brain mask used to
register native image to stereotaxic space
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1. Surface extraction by hemisphere rather than for entire brain
double number of points for better resolution
no more bad surface folds between hemispheres
2. “Fill up” ventricles, mask out sub-cortical gray, cerebellum,
brain stem
simplified surface to analyze cortex only
3. Dilate brain mask to include at least one layer of csf around
gray to have a continuous csf skeleton, for better gray surface
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boundary
4. Speed optimization, convergence criterion, robustness
Many thanks to Oliver for items 1 and 2.'
Quality control - registration and classification
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& %' Quality control - surface extraction
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& %' Work in progress and future work
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Non-linear transformation of tag points for better classification
Masking of eyes
Non-uniformity corrections for 3T
Extension of mincbet for hyper intense voxels (may be able to
do skull-masking using t1 only)
Computations at 0.5mm voxel size (or at any size)
Co-registration of scans in longitudinal studies
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Surface registration (awaiting Maxime’s depth map)
Documentation' Using CIVET
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Quarantines in /data/aces/aces1/quarantines
For old CIVET (single brain surface):
/data/aces/aces1/quarantines/IRIX64-IP35/May-01-2006
/data/aces/aces1/quarantines/Linux-i686/May-08-2006
For new CIVET (one surface per hemisphere):
/data/aces/aces1/quarantines/Linux-i686/Sep-12-2006
Loading the environment:
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source ... /Linux-i686/Sep-12-2006/CIVET/init.csh
Starting CIVET:
... /Linux-i686/Sep-12-2006/CIVET/CIVET Processing Pipeline'
Summary of options:
Configuring CIVET
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-- Execution control ------------------------------------------------------
-spawn Use the perl system interface to spawn jobs
-sge Use SGE to spawn jobs.
-pbs Use PBS to spawn jobs
-- PBS options ------------------------------------------------------------
-pbs-queue Which PBS queue to use [short|medium|long]
[default: long]
-pbs-hosts Colon separated list of pbs hosts [default:
yorick:bullcalf]
-- SGE options ------------------------------------------------------------
-sge-queue Which SGE queue to use [default: aces.q]
-- File options -----------------------------------------------------------
-sourcedir Directory containing the source files.
-targetdir Directory where processed data will be placed.
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-prefix File prefix to be used in naming output files.
-id-subdir Indicate that the source directory contains
sub-directories for each id
-id-file A text file that contains all the subject id's
(separated by space, tab, return or comma) that
CIVET will run on.' Configuring CIVET
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-- Pipeline options -------------------------------------------------------
-template Define the template for image processing (0.50,
0.75, 1.00, 1.50, 2.00, 3.00, 4.00, 6.00).
[default: 1.00]
-registration-model Define the target model for registration.
-registration-modeldir Define the directory of the target model for
registration.
-- CIVET options ----------------------------------------------------------
-multispectral Use T1, T2 and PD native files for tissue
classification.
-spectral_mask Use T1, T2 and PD native files for brain masking.
-crop-neck Percentage of height to crop to remove neck for
masking.
-N3-distance N3 spline distance in mm (suggested values: 200 for
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1.5T scan (default); 25 for 3T scan). [default: 200]
-no-surfaces don't build surfaces
-no-animal don't run volumetric ANIMAL segmentation.
-thickness compute cortical thickness and blur
[tlink|tlaplace|tnear|tnormal] [kernel size in mm]' $
Configuring CIVET
-- Pipeline control -------------------------------------------------------
-run Run the pipeline.
-status-from-files Compute pipeline status from files
-print-stages Print the pipeline stages.
-print-status Print the status of each pipeline.
-make-graph Create dot graph file.
-make-filename-graph Create dot graph of filenames.
-print-status-report Writes a CSV status report to file in cwd.
-- Stage Control ----------------------------------------------------------
-reset-all Start the pipeline from the beginning.
-reset-from Restart from the specified stage.
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-reset-running Restart currently running jobs. [default]
-no-reset-running opposite of -reset-running' Configurable parameters for CIVET
Mandatory parameters:
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-prefix: name of study
-sourcedir: input directory
-targetdir: output directory
Optional useful parameters:
-spectral mask: to use t1, t2, pd for the brain mask
-crop-neck: to crop tall neck before registration
-N3-distance: to specify the distance parameter for N3
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(experimental, for 3T scans)
-multispectral: to use t1, t2, pd for classification
-thickness: to generate the cortical thickness' $
Running CIVET
source /data/aces/aces1/quarantines/Linux-i686/Sep-12-2006/CIVET/init-sge.csh
/data/aces/aces1/quarantines/Linux-i686/Sep-12-2006/CIVET/CIVET_Processing_Pipeline
-spectral_mask -prefix SPGR -crop-neck 10
-sourcedir /data/node7/claude/tests/native/ec
-targetdir /data/node7/claude/tests/ec
-thickness tlink 20 -sge -run 2021_2 2021 2039A 2039B > log
& %' CIVET directories for each subject
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native: images in native space
final: images in stereotaxic space
transforms: linear and non-linear transformations
mask: brain masks
classify: classified image
surfaces: white and gray surfaces
thickness: cortical thickness maps
segment: ANIMAL segmentation
verify: QC images *.png
logs: execution logs for stages (contains status files .failed,
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.finished, .running, .lock)
temp: temporary working files
Look for status files in logs and QC images in verify.' $
Discussion
Using a unified versioned pipeline for all analyses
Choice of metrics for validation of pipeline
Configuring and running CIVET/CLASP
Approximate timelines for developments and future releases
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Post-meeting notes
Cortical thickness is currently computed in Talairach space and
must be transformed to native space before analysis
PMP will be improved to zip/unzip inputs and outputs to
conserve disk space
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