1. Morphometry BIRN Semi-Automated Shape Analysis (SASHA) JHU (CIS): M. F. Beg, C. Ceritoglu, A. Kolasny, M. I. Miller, R. Yashinski MGH (NMR): B. Fischl; BWH (SPL): S. Pieper; UCLA (LONI): D. Rex, A. W. Toga Washington University (CNL): R. L. Buckner Introduction: Conclusions: Morphometry BIRN Integration issues when pooling software analysis and visualization tools are a challenging problem. Using a common data format to span multiple applications allows for integration and usability. Creating translation tools between formats is critical to integrating independent applications into a complete analysis pipeline. We have processed data from a donor site (Wash. Univ.) with the Shape Analysis Processing Pipeline. Procedures for converting MGH surface data to LDDMM volumes data format have been developed. LDDMM natively interfaces with Slicer using the Visualization Toolkit (VTK) file format. With the processing pipeline in place, efforts to streamline the processing of large image data sets are taking place. Statistical Analysis of large groups of image data is under way. BIRN Virtual Data Grid Goals: Requirements: Seamless and robust integration of MRI analysis and visualization tools that have been independently developed at different sites. Enable analysis of MRI data from any other site. To develop a processing pipeline across multiple institutional sites that segments sub cortical structures from structural MRI data, computes the geodesics in the space of infinite dimensional diffeomorphisms, visualizes results and enables statistical analyses of the results. To use this approach to test the hypothesis that hippocampal shape differs between patients with Alzheimer’s disease and demographically matched healthy controls. Data Donor Site De-identification Wash. Univ. T 1 structural MRI Alzheimer’s and Age-matched controls Image Header: Subject: Juan Perez Patient ID: 911 … Image Header: Subject: anon BIRN ID: 9284ka9e23sd… … Data Upload 1 MGH Freesurfer Cortical & Subcortical segmentations 2 http://surfer.nmr.mgh.harvard.edu/ 4 3 Anatomical segmentation using FreeSurfer Structural MRI data acquired 2 4 1 3.The derived segmented data (e.g., the hippocampus surfaces) can be downloaded by the JHU site and used for shape analysis using their Large Deformation Diffeomorphic Metric Mapping tool (LDDMM). These results are uploaded as new derived data in the BVDG. JHU Large Deformation Diffeomorphic Metric Mapping Shape Analysis of Segmented Structures http://www.cis.jhu.edu/software/lddmm Shape Analysis using LDDMM 3 1.3D Structural MRI data with good gray-white matter contrast-to-noise ratio is acquired at a participating site. In order to be shared, the image data has to be de-identified within the site’s firewall: patient information is removed from the image headers and face information is stripped from the images while leaving the brain intact. The de-identified data is then uploaded to the BIRN Virtual Data Grid (BVDG) where it can be accessed by other participating sites. 2.The de-identified structural brain MRI data is automatically segmented using MGH’s FreeSurfer morphometry tools. The segmentation results are uploaded to the BVDG as derived data (surfaces, volumes, labels). 4.The combined morphometric results (surfaces, volumes, labels, deformation fields) can be viewed from the BVDG using 3D Slicer as the common visualization platform. UCLA Brigham and Women’s Hospital Johns Hopkins University Brain Imaging and Analysis Center BWH 3D Slicer Visualization of segmentation and shape analysis results http://www.slicer.org/ Visualization of results from a common platform 4