Outline. Overview of CIVET and CLASP. Identification of problems

Transcription

1 $ Outline Overview of CIVET and CLASP Identification of problems Recent improvements brain-masking non-uniformity corrections registration surface extraction Work in progress and future work Discussion

2 Image-processing pipeline CIVET native t1, t2, pd non uniformity corrections linear registration transformation to stx space brain masking classification pve CLASP ANIMAL segmentation surface extraction

3 $ Observed problems 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 Surface extraction is slow To fix these problems, we must understand why they occur?

4 $ Brain Masking - Skull and Eyes

5 Brain Masking Truncated Mask occipital lobes temporal lobe The cortical thickness will be wrong in these regions. The problem is due to failure of non-uniformity corrections.

6 $ Failed linear registration without masking transformed target combined The source image is transformed inside the target image! Application of models (e.g. tag file for classification) will not be optimal.

7 $ Failed linear registration due to tall neck Failed non-linear registration If linear registration is not good to start with or if brain mask contains skull Non-linear registration is used to mask out brain stem, cerebellum, sub-cortical gray, ventricles, (eyes)

8 Under-expanded gray surface old CLASP new CLASP Simply do more iterations and use oversampling!

9 Susceptibility artefacts nasal cavities ear canals blood vessels Artefacts can alter tissue classification locally.

10 How good is brain-masking? mincbet now uses 20K surface instead of 5K surface 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 cortical surface performs reliably if image has been properly registered to stereotaxic space (uses a model mask)

11 $ Improving non-uniformity corrections 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 structures hierarchical approach (200mm, 100mm, 75mm, 50mm, 25mm)?

12 Non-uniformity corrections for 3T scan spline distance at 200mm suitable for 1.5T spline distance at 25mm looks interesting for 3T before N3 after N3 (25mm) BrainVisa has a specific correction for 3T scans

13 Improving registration Correction of non-uniformities before registration 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 Cropping tall neck improves quality of brain mask used to register native image to stereotaxic space

14 Improving CLASP 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 boundary 4. Speed optimization, convergence criterion, robustness Many thanks to Oliver for items 1 and 2.

15 Quality control - registration and classification

16 Quality control - surface extraction

17 $ Work in progress and future work 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 Surface registration (awaiting Maxime s depth map) Documentation

18 Using CIVET Quarantines in /data/aces/aces1/quarantines For old CIVET (single brain surface): /data/aces/aces1/quarantines/irix64-ip35/may /data/aces/aces1/quarantines/linux-i686/may For new CIVET (one surface per hemisphere): /data/aces/aces1/quarantines/linux-i686/sep Loading the environment: source... Starting CIVET: /Linux-i686/Sep /CIVET/init.csh... /Linux-i686/Sep /CIVET/CIVET Processing Pipeline

19 Configuring CIVET Summary of options: -- 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. -prefix File prefix to be used in naming output files. -id-subdir -id-file Indicate that the source directory contains sub-directories for each id A text file that contains all the subject ids (separated by space, tab, return or comma) that CIVET will run on.

20 Configuring CIVET -- 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 1.5T scan (default); 25 for 3T scan). [default: 200] -no-surfaces -no-animal -thickness dont build surfaces dont run volumetric ANIMAL segmentation. compute cortical thickness and blur [tlink tlaplace tnear tnormal] [kernel size in mm]

21 $ 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. -reset-running Restart currently running jobs. [default] -no-reset-running opposite of -reset-running

22 Configurable parameters for CIVET Mandatory parameters: -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 (experimental, for 3T scans) -multispectral: to use t1, t2, pd for classification -thickness: to generate the cortical thickness

23 $ Running CIVET source /data/aces/aces1/quarantines/linux-i686/sep /civet/init-sge.csh /data/aces/aces1/quarantines/linux-i686/sep /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_ A 2039B > log

24 $ CIVET directories for each subject 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,.finished,.running,.lock) temp: temporary working files Look for status files in logs and QC images in verify.

25 $ 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

26 $ 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