Overview
CIVET is a human brain image-processing pipeline for fully-automated corticometric, morphometric and volumetric analyses of magnetic resonance (MR) images. The pipeline is composed of a set of Perl scripts and high-level C/C++ programs.
CIVET starts by a 9 or 12-parameter linear/affine registration of the MR images (T1-weighted, T2-weighted and Proton Density weighted) from native to stereotaxic space, using the average MNI ICBM152 model (or any other volumetric model that is available) as the target of registration. Once the images have been transformed to stereotaxic space, at 0.5mm or 1.0mm voxel size, the images are corrected for radio-frequency non-uniformities and a brain mask is computed. A non-linear transformation is then computed from the subject in stereotaxic space to the model.
The next step in CIVET is to perform the tissue classification into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF), either based on the T1-weighted image only, or on multispectral data (T1, T2, PD). The classification is comprised of two steps: a discrete classification based on tag points, followed by the evaluation of the partial volumes for the tissue classes.
The brain is split into the left and right hemispheres for the purpose of surface extraction. The boundary between cortical GM and subcortical WM (referred to as the white matter surface) is first extracted. The pial surface, or the boundary betwen the cortical GM and the extra-cortical CSF (referred to as the GM surface) expands outwards from the WM surface to the CSF. Each default surface (per hemisphere) is composed of 81,920 triangles (polygons) and 40,962 vertices. High resolution surfaces are also available at 327,680 polygons and 163,842 vertices.
The surfaces are registered to the MNI ICBM152 surface template (or any other surface model that is available). Surface registration as such allows for group comparisons by interpolating vertex-based cortical measurements at reference vertices on the template. The cortical thickness is computed by evaluating the distance, in mm, between the original WM and GM surfaces transformed back to the native space of the original MR images, then interpolated onto the surface template. Vertex-based areas and volumes are, on the other hand, computed directly on the resampled surfaces and measure local variations of area/volume contraction and expansion relative to the vertex distribution on the surface template.
Regional maps are produced based on a lobar parcellation of the surfaces (major lobes, AAL, or DKT-40). Other measurements such as mean curvature, gyrification index, and total cortical area are also computed.
A number of figures, tables, and reports are automatically produced to assist in the process of quality control and data analysis.
What’s new in CIVET-2.1.1?
CIVET-2.1.1 is nearly identical to CIVET-2.1.0 with the following exceptions:
- N3 intensity normalization:
- Multi-platform compilation:
- Source code access:
What’s new in CIVET-2.1.0?
CIVET-2.1.0 is based on CIVET-2.0.0 in which the marching-cubes algorithm and the white surface t1-gradient calibration were introduced.
- Marching-cubes algorithm:
- White surface t1-gradient calibration:
- Gray surfaces:
- Tissue classification:
- Cortical thickness:
- Native space:
- Study prefix:
- Quality control:
- AAL parcellation:
It is recommended to run CIVET-2.1.0 with the following options:
- N3 distance: d=200 for 1.5T; d=125 for 3.0T (based on new N3)
- stereotaxic voxel size resolution: 0.50mm
- advanced PVE options: iterative correction, masking, sub-cortical tissue class
- cortical thickness method: tlaplace or tfs, never tlink
Full summary of major changes between CIVET 1.1.12, CIVET 2.0.0, and CIVET-2.1.0:
************************************************************************* New in September, 2016 quarantine, version 2.1.0 - allow native scans in stereotaxic space without linear transformation - pve with iterative correction of thresholds, masking of cerebellum and brainstem, sub-cortical tissue class - sub-cortical tissue class to VBM - study prefix is optional - compute several thickess methods at once - increase search radius for white surface gradient correction and use Taubin smoothing - add partial volume estimates to Laplacian field for gray surface expansion - QC: add gradient, distortion and convergence graphs - average_surface_builder: allow non-linear mode, add Taubin smoothing ************************************************************************* New in September, 2014 quarantine, version 2.0.0 1 - marching-cubes algorithm for extraction of initial white surface 2 - new calibration of white surface at GM-WM t1-gradient 3 - improved node movement in surface fit program 4 - corrected functional for Laplacian constraint in surface fit program (for gray surface) 5 - improved surface registration 6 - new surface registration models based on marching-cubes 7 - simplified CIVET QC pipeline 8 - corrected pve classification at 0.50mm volume template 9 - all changes listed below for 1.1.13 10 - ANIMAL volumetric lobar segmentation 11 - corrected fwhm for surface smoothing (was off by sqrt(2) in 1.1.12) Note: This version requires the package surface-extraction-3.0.2 or higher. More to come soon: 1 - classification and surface extraction in native space. 2 - intermediate model for linear and non-linear volume registration. 3 - Plug-in for alternate population templates 4 - removal of self-intersections in .sm file for surface registration (Maxime Boucher) 5 - Longitudinal surface registration and QC ************************************************************************* New in July, 2013 quarantine, version 1.1.13 (not released) 1 - bug fixed in N3 to be invariant to voxel size (requires N3-1.12.0 and EBTKS-1.6.4) 2 - high-resolution surfaces at 320k polygons 3 - surface registration on high-resolution surfaces, true multi-resolution algorithm (faster) 4 - improved medial cut through the corpus callosum 5 - processing at voxel sizes of 0.5mm and 1.0mm 6 - speed optimizations for pve 7 - speed optimizations for Laplacian field at 0.5mm 8 - cropping of non-linear xfm grid file to save disk space and to reduce peak memory 9 - masking of hippocampus and amygdala for icbm152nl_09s model (0.5mm) 10 - masking of hippocampus and amygdala for ADNIhires model (0.5mm) 11 - improved schedule for white matter surface extraction (smoother convergence, scaled starting ellipsoid) 12 - new ICBM surface average for scaled ellipsoid 13 - clean-up of connected pieces of white matter (partial volumes) 14 - new pipeline to build average surfaces from CIVET output