NAMIC Wiki - User contributions [en]

DBP2:UNC:Cortical Thickness Roadmap

2007-11-14T16:19:29Z

Hcody:

== Objective ==

We would like to create an end-to-end application within Slicer3 allowing individual and group analysis of regional and local cortical thickness.
Such a workflow applied to the young brain (2-4 years old) is one goal of the [[DBP2:UNC | UNC DBP]]. This page describes the technology roadmap for cortical thickness analysis in the NA-MIC Kit. The basic components necessary for this end-to-end application are:
:* Tissue segmentation: Should be multi-modality, correcting for intensity inhomogeneity and work on non-skull-stripped data.
:* Cortical thickness measurement: Local cortical thickness needs measurements at every location of the white-gray matter boundary, as well as at the gray-csf boundary. Regional analysis does not need such a dense measurement.
:* Cortical correspondence: Local analysis needs a full correspondence on both white-gray boundary and gray-csf boundary.
:* Statistical analysis/Hypothesis testing: Measurements need to be compared and tested localy incorporating multiple-comparison correction, correlative analysis would be necessary too.

== Roadmap ==

Starting with several MRI images (weighted-T1, weighted-T2...) we want to obtain cortical thickness maps for each subject, compute cortical correspondences between subjects, and analyze the cortical thickness at these corresponding locations.
Ultimately, the NA-MIC Kit will provide a workflow for individual and group analysis of cortical thickness. It will be implemented as a set of Slicer3 modules that can be used interactively within the Slicer3 application as well as in batch on a computing cluster using BatchMake.

Next we discuss the main modules and details of current status and development work:

=== White matter/gray matter segmentation ===
:* UNC has an open source segmentation tool called itkEMS ([http://www.ia.unc.edu/dev/download/itkems/index.htm binary download]) implemented in an ITK framework for segmenting white matter and gray matter in the young brain. This technique will be converted into a Slicer3 [[Slicer3:Execution Model Documentation | command line module]].
:** Since this segmentation technique exists in an ITK framework, the integration into Slicer3 is low risk and should be completed over the next couple of months (mid fall).
:* UNC will also investigate adapting the [[Slicer3:EM | Slicer3 EM Segment module]] to their young brain studies. Here, UNC will adapt the UNC atlas of the 2 year old brain to provide priors for the EM Segment module.
:** This will be a good test case for applying the Slicer3 EM Segment module to a slightly different application. UNC should work through the training material on the Slicer3 EM Segment module and then refer to Brad Davis and Kilian Pohl as needed.
:** This also should be completed before the AHM.

=== Local cortical thickness measurement ===
:* UNC has an algorithm to measure a local cortical thickness given a labeling of white matter and gray matter. This technique will be converted into a Slicer3 command line module.
:** This technique is non-symmetric and sparse (only computing distances where they can be computed reliably).
:** This module is well suited for regional analysis, but additional work to interpolate dense measurements along the boundaries from the sparse ones would be necessary for a local analysis.
:** It is expected that this module should be available before the AHM (for the current, sparse measurements).
:* Marc Niethammer developed a [[Algorithm:Harvard:Thickness Slicer3 Module|technique]] at a previous project week that would be symmetric. This could be used for the local cortical thickness analysis, if our comparison studies show appropriate results.

=== Local correspondence ===
:* Regional as well as local subject comparisons are needed.
:* Regional analysis will require precise deformable registration to a young brain atlas.
:** NA-MIC toolkit can be applied here, Slicer 3 has a b-spline based MI registration which needs to be tested.
:*** The Slicer 3 registration modules have no possibility to save/load the computed transform (only the transformed image is saved), this is a necessity for this application! We need an updated version of the registration
:*** Testing will be performed to see whether registration quality is appropriate
:** Parcellation atlas for regional analysis is the current atlas used by the DBP..
:* Local analysis requires techniques which are not currently in the NA-MIC Kit
:** Freesurfer can be used for the local analysis (but it is not in the NA-MIC Kit).
:** Ipek Oguz is developing local analysis tools and should be available in Fall 2008. The tool necessitates the following steps (also performed within Freesurfer):
:*** 1) spherical topology of white matter: Marc Niethammer has Slicer module for this step, testing will be necessary to ensure appropriateness in the cortical case.
:*** 2) inflation of cortical surface: A Slicer module exists for this step, but likely not with the properties. Tests and possible additional implementation will need to be done.
:*** 3) computation of correspondence on inflated surface using particle system: No module exists, first prototype programs are in development in collaboration with Utah (I Oguz, J Cates).

=== Hypothesis testing ===

Regional testing can be done with standard statistical tools as there are a limited, relatively small number of regions.

For local analysis, such standard statistical tools are not applicable. Currently there is no local hypothesis testing framework with multiple comparison and generalized linear model computation in Slicer 3. Freesurfer has such a framework readily available. The UNC shape analysis testing framework and extensions of it should be applicable here.
:* Freesurfer can compute group difference and correlative analysis on local cortical thickness.
:* UNC shape analysis testing framework allows local hypothesis testing with multiple comparison correction, but no General Linear Model. An extension in this regard is currently in development (M Styner). A Slicer 3 module allowing direct group differences will be available by AHM.
:* An alternative possible testing framework is also being developed at UNC (outside of NAMIC) as an open source tool.

=== Performance characterization and validation ===
:* Characterize response based on signal noise, patient motion, etc.
:* Comparison to other tools (FreeSurfer, itkEMS, UNC cortical thickness).

=== Schedule ===

* Tissue segmentation
:* '''09/11/2007''' - Slicer registration modules input/output transforms (J Miller)
:* '''09/11/2007''' - Slicer module to resample data through a transformation (J Miller)
:* '''09/11/2007''' - B-spline transform nodes (A Yarmarkovich)
:* '''10/15/2007''' - White matter/gray matter segmentation of the young brain using itkEMS as a Slicer3 module (C Vachet) - at AHM -- DONE --
:* '''12/01/2007''' - White matter/gray matter segmentation of the young brain using the Slicer3 EM Segment module (C Vachet) - at AHM
:* '''xx/xx/xxxx''' - Registration to atlas in EM Segment (B Davis)
:* '''12/15/2007''' - Comparison study itkEMS vs EM Segment in Slicer 3 (C Vachet) - at AHM
* Cortical thickness
:* '''12/23/2007''' - UNC Cortical thickness measurement as a Slicer3 module (C Vachet) - at AHM -- DONE --
:* '''12/23/2007''' - Niethammer's Laplacian Cortical thickness measurement module code working at UNC as a Slicer3 module (C Vachet) - at AHM
:* '''03/01/2008''' - Regional comparison between UNC and Laplacian cortical thickness (C Vachet)
* Cortical correspondence
:* '''12/15/2007''' - Slicer 3 Deformable registration of young brain regional atlas (C Vachet) - at AHM
:* '''04/01/2008''' - Regional analysis of cortical thickness as a Slicer3 module (C Vachet), develoment work is necessary
:* '''11/01/2007''' - Niethammer Spherical topology tool tested on cortical data (I Oguz)
:* '''12/01/2007''' - Inflation/Unfolding Slicer module tested on cortical data (I Oguz)
:* '''06/01/2008''' - Slicer 3 module for cortical correspondence on inflated surface (I Oguz)
:* '''08/01/2008''' - Fully evaluation of cortical computation on autism DBP datasets (C Vachet)
* Statistical analysis and Hypothesis testing
:* '''12/01/2007''' - Slicer 3 Statistical Shape Analysis module (C Vachet, N Augier)
:* '''04/01/2008''' - Slicer 3 Statistical Cortical Thickness Analysis module (C Vachet)
:* '''06/01/2008''' - GLM model for UNC statistical shape analysis (M Styner)
:* '''xx/xx/xxxx''' - Nonparametric hypothesis testing in ITK (J Miller)
* Workflow/cohesive tools
:* '''06/01/2008''' - Groupwise regional analysis of cortical thickness as a NA-MIC Workflow (C Vachet)
:* '''02/01/2009''' - Groupwise local analysis of cortical thickness as a NA-MIC Workflow (C Vachet)

=== Team and Institute ===
*Co-PI: Heather Cody Hazlett, PhD, (heather_cody at med.unc.edu, Ph: 919-966-4099)
*Co-PI: Joseph Piven, MD
*NA-MIC Engineering Contact: Jim Miller, GE Research
*NA-MIC Algorithms Contact: Martin Styner, UNC

DBP2:UNC

2007-03-16T22:48:44Z

Hcody: /* Team and Institute */

Back to [[DBP2]]

* '''Title:''' Longitudinal MRI study of early brain development in neuropsychiatric disorder: UNC Autism Study

==Team and Institute==
*Co-PI: Heather Cody Hazlett, PhD, (heather_cody at med.unc.edu, Ph: 919-966-4099)
*Co-PI: Joseph Piven, MD
*NA-MIC Engineering Contact: Jim Miller, GE Research
*NA-MIC Algorithms Contact:

* '''Affiliation/Institution:''' UNC Chapel Hill, Department of Psychiatry and the Neruodevelopmental Disorders Research Center NDRC

* '''Science:''' Multiple lines of converging evidence (from MRI, post-mortem and head circumference studies) indicate that brain enlargement in autism is a real phenomenon. However, the onset, trajectory and pattern of this enlargement (in brain tissues, regions and structures), relationship to developing neural circuitry and clinical features; and, pathogenesis, are not yet clear. Results from our longitudinal MRI study of brain development (2 years with follow-up at 4 years) demonstrate robust generalized enlargement of white and gray matter volume in cerebral cortex in autistic individuals (N= 51) by age 2 yrs. The MRI and earlier head circumference data strongly suggest a period of substantial brain overgrowth in autistic individuals between age 12 and 24 months, continuing into ages 4 and 5. This study will provide critical information about the trajectory of brain growth (regions, tissues, structures and fiber tracts) as measured on MRI and DTI, the potential relationship to clinical features; and underlying genetic etiology. These results will provide important insights into developmental brain and behavioral phenotypes, and neurobiological mechanisms in autism.

* ''' Benefits to NA-MIC'''<nowiki>: There is a lack of appropriate tools for processing of pediatric MRI, a challenging topic since pediatric MRI differs significantly from adult MRI due to variable brain shape and the process of maturation/myelination which are reflected in nonlinear shape/volume changes but also regional change of white matter:. Working on a toolkit for the community would have a large impact, in particular also in view of existing and soon to be available databases of normative pediatric MRI (PI Alan Evans). Access to the UNC longitudinal pediatric MRI data representing a period of moderate but significant brain growth can spawn off interesting new software methodology developments from Core-1. Besides existing multi-modal MRI data, the UNC group has a very large set of segmented data (subcortical structures measured with very high reliability (0.92 up to 0.99) for over 140 MRI data sets(hippocampus, amygdale, putamen, pallide globe, caudate, ventricles) - to our knowledge the largest segmentation database of such high quality. These data could be used for shape analysis of growth trajectory and can also serve as a benchmark for novel semi-automated processing. The group has profound experience with the development of novel segmentation protocols (</nowiki>http://www.psychiatry.unc.edu/autismresearch/MRI_PAGE.htm) and the design of large-scale validation of segmentation methodology (see Yushkevich et al., 2006, NeuroImage, http://dx.doi.org/10.1016/j.neuroimage.2006.01.015). Moreover, the groups experience with state-of-the-art ITK/vtk processing tools will help to critically assess and improve the NA-MIC toolkit’s development from the viewpoint of users involved in large clinical studies. The processing of a relatively large database needs highly automated processing “pipelines”, i.e. co-registration of multi-modal data, atlas-to-template registration, automatic tissue segmentation, lobe parcellation, MRI-DTI registration, ROI analysis, and statistical analysis. This data therefore would be an excellent testbed for new automated Slicer 3 processing. A growth-rate analysis might have to include new methods for longitudinal image analysis, cortical thickness and cortical folding pattern analysis, methods not yet developed for the NA-MIC toolkit but required for human brain studies.

* ''' Benefits to UNC NDRC group: ''' The UNC autism research group will have access to NAMIC tools not yet available for analysis, which will expose them to new tools and procedures beyond the ones locally developed. This will significantly expand their processing capabilities but also will allow them to do research within a larger team of leading image analysis research groups. New tools applied to the existing longitdudinal autism pediatric study, including raw image data and processed anatomical structures, are most likely lead to publications demonstrating the processing capabilities and versatility of the NAMIC toolkit.

File:Namic ahm jan07 longitud aut study.ppt

2007-01-10T06:07:37Z

Hcody: UNC Autism study presentation for AHM Jan 2007

UNC Autism study presentation for AHM Jan 2007