Difference between revisions of "NA-MIC NCBC Collaboration:3D Shape Analysis for Computational Anatomy"

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[[Image:JHUCollaboration.jpg|thumb|right|300px|Deformation of hippocampal surface in 27 patients with MDD relative to hippocampus of 42 healthy comparison subjects]]
 
[[Image:JHUCollaboration.jpg|thumb|right|300px|Deformation of hippocampal surface in 27 patients with MDD relative to hippocampus of 42 healthy comparison subjects]]
 
==Abstract==
 
==Abstract==
Musculoskeletal finite element (FE) analysis is an invaluable tool in orthopedic-related research. While it has provided significant biomechanical insight, the demands associated with modeling the geometrically complex structures of the human body often limit its utility. The often-prohibitive amount of model development time is further compounded by the time required to process medical image datasets to identify the distinct anatomical structures of interest. Yet this process is a necessary preprocessing step for model development. As a result, most of the analyses reported in the literature refer to 'average' bone geometry. The broad objective of our research plan is to integrate and expand methods to automate the development of specimen- / patient-specific finite element (FE) models into the NA-MIC toolkit. In pursuit of this objective we propose to merge unique technologies to automate image dataset segmentation; material property extraction and assignment; and direct FE model development (automated meshing). While direct physical scans of the bones of interest will be used to validate the automated image segmentation routines, experimental cadaveric contact stress measurements will provide a standard against which to validate the FE contact formulations. Furthermore, the FE models generated by our software package will be compared to models of the same bone(s) created via a commercial pre-processing package. While the bones/joints of the upper extremity represent the primary structures of interest proposed in this application, the tools will be applicable to many orthopedic applications. In addition to expanding the NA-MIC toolkit beyond the brain, the proposed project will expand the image segmentation routines and finite element meshing routines currently available. This proposal will ultimately yield specimen-specific FE models of the various joints of the upper extremity. Such models will position us to provide information about the load transfer, characteristics of the normal joints and in the future to demonstrate, for example, the effects of ligamentous instabilities, posttraumatic misalignments, fractures, and various surgical procedures.
+
The long term goal of Computational Anatomy (CA) is to create algorithmic tools that aid basic and
 +
clinical neuroscientists in the analysis of variability in anatomical structures at different scales. The
 +
difficulty is the complexity of anatomical substructures and the large variation across subjects. It is
 +
proposed to develop an open-source pipeline for 3D statistical shape analysis of anatomical
 +
variations from a population of anatomical structures. The overall aim is to integrate 3D Slicer
 +
application and ITK software library with the statistical shape analysis pipeline being disseminated by
 +
the Biomedical Informatics Research Network and thus enable the wider neuroimaging community to
 +
efficiently analyze anatomical variations in disease. The first aim is to standardize shape deformation
 +
vectors generated by several CA methods such as the Large Deformation Diffeomorphic Metric
 +
Mapping (LDDMM) developed at the Center for Imaging Science at Johns Hopkins University and the
 +
Finite Element Method for Deformable Registration (FEMDR) used in ITK. This will allow shape
 +
vectors to be used by both global metric classifier analysis in classifying diseased shapes and
 +
Gaussian Random Field (GRF) model analysis in localizing shape changes in disease. The two
 +
methods will be unified to provide a new metric classifier based on the data generated by GRF. In the
 +
final stage, hypothesis testing will be used to correlate global metric classification with localized
 +
shape changes. The second aim is to construct anatomical atlases needed for analysis of shape
 +
vectors. These atlases will be generated from segmented hippocampal and amygdala structures in
 +
already acquired populations of children, adolescents and young adults in neuroimaging studies of
 +
major depression disorder (MDD) at Washington University at St Louis. As a major public health
 +
burden, MDD provides the biological testbed for the pipeline from which probabilistic atlases will be
 +
generated. The third aim is to integrate the software libraries with the pipeline by leveraging the
 +
power and flexibility of the 3D Slicer software and ITK libraries developed by NA-MIC, Kitware and
 +
others. The fourth aim is to implement modules for visualization of the analysis of shape vectors in 3D
 +
Slicer. The fifth aim is to implement a stand-alone version of Medical Reality Markup Language
 +
(MRML) independent of 3D Slicer. This will allow for the propagation of MRML as a standard format
 +
for future neuroimaging applications.
  
 
==Grant#==
 
==Grant#==

Revision as of 07:20, 10 July 2009

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Deformation of hippocampal surface in 27 patients with MDD relative to hippocampus of 42 healthy comparison subjects

Abstract

The long term goal of Computational Anatomy (CA) is to create algorithmic tools that aid basic and clinical neuroscientists in the analysis of variability in anatomical structures at different scales. The difficulty is the complexity of anatomical substructures and the large variation across subjects. It is proposed to develop an open-source pipeline for 3D statistical shape analysis of anatomical variations from a population of anatomical structures. The overall aim is to integrate 3D Slicer application and ITK software library with the statistical shape analysis pipeline being disseminated by the Biomedical Informatics Research Network and thus enable the wider neuroimaging community to efficiently analyze anatomical variations in disease. The first aim is to standardize shape deformation vectors generated by several CA methods such as the Large Deformation Diffeomorphic Metric Mapping (LDDMM) developed at the Center for Imaging Science at Johns Hopkins University and the Finite Element Method for Deformable Registration (FEMDR) used in ITK. This will allow shape vectors to be used by both global metric classifier analysis in classifying diseased shapes and Gaussian Random Field (GRF) model analysis in localizing shape changes in disease. The two methods will be unified to provide a new metric classifier based on the data generated by GRF. In the final stage, hypothesis testing will be used to correlate global metric classification with localized shape changes. The second aim is to construct anatomical atlases needed for analysis of shape vectors. These atlases will be generated from segmented hippocampal and amygdala structures in already acquired populations of children, adolescents and young adults in neuroimaging studies of major depression disorder (MDD) at Washington University at St Louis. As a major public health burden, MDD provides the biological testbed for the pipeline from which probabilistic atlases will be generated. The third aim is to integrate the software libraries with the pipeline by leveraging the power and flexibility of the 3D Slicer software and ITK libraries developed by NA-MIC, Kitware and others. The fourth aim is to implement modules for visualization of the analysis of shape vectors in 3D Slicer. The fifth aim is to implement a stand-alone version of Medical Reality Markup Language (MRML) independent of 3D Slicer. This will allow for the propagation of MRML as a standard format for future neuroimaging applications.

Grant#

1R01 EB008171-01A1

Key Personnel