2004 All Hands Meeting Morphometry BIRN Bruce Rosen, MD PhD Jorge Jovicich PhD Steve Pieper, PhD David Kennedy, PhD

Morphometry BIRN: The PI “ The Principal Investigator, Dr. Rosen, does not have training or experience in computer science, programming, database design or digital networking…” mBIRN Summary Statement – 8/04

Morphometry BIRN “Strengths of the application include the substantial public health significance of the proposed mBIRN, a well qualified team of site investigators…, strong institutional support enhanced by the existing BIRN- CC, development of novel shape-modeling approaches, segmentation, and DTI methods, and the innovation of a number of project goals to link visual imaging with machine learning methods” “Jorge Jovicich is to be commended for the skills he brings to this work. Leaders such as Randy Buckner, David Kennedy, Michael Miller, and Arthur Toga are invaluable assets for such an endeavor”

 Morphometry BIRN Goals  Progress Highlights  Future Work Morphometry BIRN: Outline

 Overall Goal: Develop capability to analyze and mine data acquired at multiple sites using processing and visualization tools developed at multiple sites Morphometry BIRN

 Overall Goal: Develop capability to analyze and mine data acquired at multiple sites using processing and visualization tools developed at multiple sites  Context: Human Brain MR Based Morphometry  Initial Application:Alzheimer’s, Depression, Ageing Brain  Participants: MGH, BWH, Duke, UC Los Angeles, UC San Diego, Johns Hopkins, UC Irvine, Washington University, MIT Morphometry BIRN

Human Data Protection Multi-site data acquisition Data Upload Integration and Application of Processing Tools Human Imaging Database Morphometry BIRN: Progress Simplified diagram of building blocks SRB

Human Data Protection Multi-site data acquisition Data Upload Integration and Application of Processing Tools Human Imaging Database Morphometry BIRN: Progress Simplified diagram of building blocks SRB

Raw dataDe-faced data De-facing: automated de-facing without brain removal Pipeline: image formats, BIRN ID generation, defacing, QA, upload Accomplishment: Developed a robust automated methods for bulk MRI de-identification and upload to database (diverse inputs, sharable outputs, common package) De-identification and Upload Pipeline UCSD (fMRI): A. Bischoff, C.Notestine, B. Ozyurt, S. Morris, G.G. Brown MGH (NMR): B. Fischl BWH (SPL): S. Pieper UCI: D. Wei Duke: B. Boyd Morphometry BIRN: Progress

Human Data Protection Multi-site data acquisition Data Upload Integration and Application of Processing Tools Human Imaging Database Morphometry BIRN: Progress Simplified diagram of building blocks SRB

Multi-site Structural MRI Data Acquisition & Calibration Methods: common acquisition protocol, distortion correction, evaluation by scanning human phantoms multiple times at all sites MGH (NMR): J. Jovicich, A. Dale, D. Greve, E. Haley BWH (SPL): S. Pieper UCI: D. Keator UCSD (fMRI): G. Brown Duke University (NIRL): J. MacFall Corrected Uncorrected Image intensity variability on same subject scanned at 4 sites Accomplishment: develop acquisition & calibration protocols that improve reproducibility, within- and across-sites Morphometry BIRN: Progress

Human Data Protection Multi-site data acquisition Data Upload Integration and Application of Processing Tools Human Imaging Database Morphometry BIRN: Progress Simplified diagram of building blocks SRB

Shared Tools for Data Analysis Tool Site Freesurfer MGH Slicer BWH LONI Pipeline UCLA LDDMM Johns Hopkins Query Interface UCSD Morphometry BIRN: Progress Shared Data DataSite AD BWH/MGH AD UCSD AD WashU Depression Duke MCIUCI

Integration and Application of Processing Tools Various projects driving developments: Multi-site Imaging Research in Analysis of Depression Data from one site processed with tools of multiple sites Multi-site Morphometry in Analysis of Alzheimer’s Disease Data from multiple sites processed with tools of one site Semi-Automated Shape Analysis Project Data from BIRN sites processed with tools of various sites Morphometry BIRN: Progress

Integration and Application of Processing Tools Projects driving developments: Multi-site Imaging Research in Analysis of Depression (MIRIAD) Data from one site processed with tools of multiple sites Multi-site Morphometry in Analysis of Alzheimer’s Disease Data from multiple sites processed with tools of one site Semi-Automated Shape Analysis Project Data from BIRN sites processed with tools of various sites Morphometry BIRN: Progress

Duke Archives UCLA AIR Registration and Lobar Analysis BWH Intensity Normalization and EM Segmentation Duke Clinical Analysis BWH Probabilistic Atlas (one time transfer) UCSD Supercomputing Goal: analyze legacy data using automated lobar segmentation (UCLA) and cortical/subcortical segmentations (BWH) MIRIAD: Overview N=200

MIRIAD Project: Accomplishments Segmentation Duke BIRN-MIRIAD Item (semi-automated)(fully-automated) # of tissue classes3 (Fig1)23 (Fig2) Time for 200 brains400 hours1 hour Time for 200 lobe &250 hours all lobes (Fig3) and 27 regional analysis regions included above Improved computational capabilities 1 2 3

Integration and Application of Processing Tools Projects driving developments: Multi-site Imaging Research in Analysis of Depression Data from one site processed with tools of multiple sites Multi-site Morphometry in Analysis of Alzheimer’s Disease Data from multiple sites processed with tools of one site Semi-Automated Shape Analysis Project Data from BIRN sites processed with tools of various sites Morphometry BIRN: Progress

AD Project: Overview MGH Segmentation Multi-Site Data Acquisition De-identification and upload SRB UCSD N=125 BWH/MGH N=118 Multi-site Data Queries and Statistics HID Visualization and Scientific Search with 3DSlicer & Query Atlas

AD Project: Accomplishments Data sharing: Successfully tested De-identification and Upload Pipeline (DUP) Integration of data Common database schemas for clinical and derived morphometry data at different sites: Human Imaging Database (HID) Mediated queries that interrogate databases at two sites Integration of processing tools MGH subcortical segmentation completed on UCSD data Statistical tools through the BIRN Portal and HID Query Interface Data visualization and interpretation using 3DSlicer and Query Atlas

Integration and Application of Processing Tools Projects driving developments: Multi-site Imaging Research in Analysis of Depression Data from one site processed with tools of multiple sites Multi-site Morphometry in Analysis of Alzheimer’s Disease Data from multiple sites processed with tools of one site Semi-Automated Shape Analysis Project Data from BIRN sites processed with tools of various sites Morphometry BIRN: Progress

SASHA Project: Overview MGH Segmentation Data Donor Site (WashU) De-identification And upload JHU Shape Analysis of Segmented Structures SRB BWH Visualization Goal: comparison and quantification of structures’ shape and volumetric differences across patient populations Teragrid N=45

SASHA Project: Accomplishments  Data: 46 hippocampus data sets (2070 comparisons)  Each LDDMM comparison takes about 3 to 8 hours Large Deformation Diffeomorphic Metric Mapping (LDDMM) using the TeraGrid Improved computational capabilities Single PCTeraGrid  1 comparison  ~431 days  60 comparisons simultaneously  ~7 days

Morphometry BIRN: Future  MRI Calibration (J. Jovicich)  Analysis, Visualization, Tools (S.Pieper)  Computational Informatics (D. Kennedy) Outline of new extensions:

MRI Calibration: Extensions  More MRI Systems Siemens 1.5T, 3T GE 1.5T, 3T, 4T Philips 1.5T, 3T Picker 1.5T  Sources of variability corrections Gradient unwarping B 0 inhomogeneities B 1 inhomogeneities On-line motion correction  More Imaging modalities: T1-based multi-spectral morphometry T2-based multi-spectral morphometry Diffusion MRI Goal Remains: Develop acquisition protocols and correction methods that minimize multi-site image variability

EYE MOTION FLOW me FLASH FLASH me FLASH FLASH Correcting for Sources of Variability B 0 inhomogeneitiesOn-line motion correction B 1 inhomogeneities

T 2 -based multi-spectral morphometry Goal: quantify reproducibility of WM, GM, CSF, lesion segmentations Subjects with known dementia vascular lesions [Duke] Test-retest, 1.5T (Siemens, GE) + 3T (GE) + 4T (GE) PD T2 T1 FLAIR Raw Data Segmentations CSF WM GM Vascular lesion

Reproducibility of diffusion MRI Goal: quantify reproducibility of fractional anisotropy and apparent diffusion coefficient as function of: MR Signal-to-noise ratio (number of averages) [JHU] Diffusion weighting (b-value) [JHU] Echo time [JHU] Number/orientation of diffusion gradient-encoding directions [UCSD] B 0 inhomogeneity corrections (field maps, time domain reconstruction) [Duke,UCSD] EPI distortion correction Spiral blurring correction

MRI Calibration Deliveries Plan By the end of 2006 we will provide:  Protocol recommendations for multi-site, multi-fields T1-based structural multi-spectral protocol T2-based structural multi-spectral protocol Diffusion protocol  Correction recommendations that minimize variability  Software tools that perform the recommended corrections  De-identified human calibration data

Morphometry BIRN: Future  MRI Calibration (J. Jovicich)  Analysis, Visualization, Tools (S.Pieper)  Computational Informatics (D. Kennedy) Outline of new extensions:

Analysis & Visualization Work Plan  Segmentation [MGH, UCSD, BWH] Extension to 3T/4T; QA; Defacing  Shape Analysis [JHU, WashU, BIRN-CC] Portal / TeraGrid Integration; DTI Mapping; Statistics  DTI Aims [BWH, UNC, UCI] Tractography Tools; White Matter Atlases  Visualization [BWH] Interactive: Structure+Shape+Tracts+Statistics  Query Atlas [BWH, UCSD] Ontology / Informatics Integration with Analysis  Machine Learning [MIT, BIRN-CC] Hypothesis Generation and Hypothesis Visualization

Query Atlas Prototype  Cortical Parcellation by Freesurfer  User Selected Features of Interest  Literature and Other Database Queries Medline, BrainInfo, SMART Atlas, CCDB, etc…

Diffusion MRI Acquisition Six Gradient-encoding directions Baseline scans

Diffusion MRI Examples  mBIRN White Matter Atlas Under Development using Slicer (BWH) by James Fallon (UCI)

Segmentation and Tractography  Parcellation Freesurfer (MGH)  Tractography DoDTI (H.J. Park)  Visualization Slicer (BWH)  Full Integration with Slicer underway

Machine Learning Plan  Apply Existing Machine Learning Code to mBIRN Datasets Mining MIRIAD Data for Provable Hypotheses Expert Review and/or Further Testing of Proposed Hypotheses  New Machine Learning Directions Incorporate a priori Domain Knowledge and Constraints to Strengthen Clinical Hypotheses  With BIRN-CC, Integrate Machine Learning Tools in Portal for Distribution

Morphometry BIRN: Future  MRI Calibration (J. Jovicich)  Analysis, Visualization, Tools (S.Pieper)  Computational Informatics (D. Kennedy) Outline of new extensions:

Computational Informatics Work Plan  Aim 1: Where’s the Data? Local/Global Upload Raw/Derived  Aim 2: More types of Data Diffusion, T2, Genetics  Aim 3: Uses of Data Quality assurance (acquisition, processing) Querying Statistics mBIRN Information Services Knowledge Management Database Tools: HID XNAT LONI DB Ontology Haystack / Semantic Web RPDR

Clinical Measures Genotype Local Storage BIRN Rack SRB MCAT HID DUP Calibration & Analysis Tools GRID Portal Mediator Institution A BIRN Rack SRB MCAT Institution B HID … Workflow Control: - Queries (identify subject populations, extract data, etc.) - Statistical Analysis - Download Data for: > Visualization > More Statistics > More Processing - Interoperable Queries (literature, homology, other databases, etc.) Human Data Protection Standardized Acquisition Protocol Institution C Informatics Architecture Local DB

Human Imaging Database Goal: develop the image repository and relational database for clinical and derived morphometric data Cortical Summary Data by Region Subcortical Summary Data by Region BWH (SPL): J. Sacks Duke University: S. Gadde, S. Anastasiadis UCI: D. Wei JHU: A. Kolasny, R. Yashinski MGH (NMR): K. Song UCSD (fMRI): B. Ozyurt UCLA (LONI): K. Crawford BIRN CC: J. Grethe

Single or Multi-site (mediated) Query Integrated Query Functions

Query Results Integrated Query Functions

Single subject data: view, browse, download Integrated Query Functions

mBIRN Ontologies Cognitive Assessment ontology ?

BIRN Portal  Web Based Single Login to BIRN Resources Intuitive Interface Flexible to Add Tools Launch Local Visualization Tools on Downloaded Data