2004 nih building on the birn bruce rosen, md phd randy gollub, md phd steve pieper, phd ...

2004 NIH Building on the BIRN

Bruce Rosen, MD PhD

Randy Gollub, MD PhD

Steve Pieper, PhD

http://www.nbirn.net

Morphometry BIRN

What is the Morphometry BIRN? (B. Rosen)

Scientific Background and Significance (R. Gollub)

BIRN Advantages in Morphometry (S. Pieper)

The BIRN Advantage

OUTLINE: Morphometry BIRN

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: BWH, MGH, Duke, UC Los Angeles,

UC San Diego, Johns Hopkins, UC

Irvine,

Washington University

Morphometry BIRN

Human Data Protection

Multi-site data

acquisition

Data Upload

Integration and Application of

Processing Tools

Human Imaging

Database

Morphometry BIRN: Flowchart

Simplified diagram

of building blocks

SRB

Raw data De-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

*See demo

Morphometry BIRN: Reality


Multi-site data

acquisition

Data Upload


Processing Tools

Human Imaging

Database


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 CorrectedUncorrected

Image intensity variability onsame subject scanned at 4 sites


Accomplishment: develop acquisition & calibration protocols that improve reproducibility, within- and across-sites


Multi-site data

acquisition

Data Upload


Processing Tools

Human Imaging

Database


Simplified diagram

of building blocks

SRB

Shared Tools for Data Analysis

:

• Freesurfer MGH

• Slicer BWH

• LONI Pipeline UCLA

• LDDMM Johns Hopkins

Morphometry BIRN

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

* Demo at BIRN Toolbox’s session (12:00-3:00pm)† Poster available with more details

Morphometry BIRN


Projects driving developments:






MIRIAD Project: Overview

DukeArchives

UCLAAIR Registration

and Lobar Analysis

BWHIntensity Normalizationand EM Segmentation

DukeClinical Analysis

1

2

3

4

BWH Probabilistic Atlas

(one time transfer)

UCSDSupercomputing

Goal: analyze legacy data using automated lobar

segmentation (UCLA) and cortical/subcortical

segmentations (BWH)

MIRIAD Project: Accomplishments

Segmentation Duke BIRN-MIRIAD

Item (semi-automated) (fully-automated)

# of tissue classes 3 (Fig1) 23 (Fig2)

Time for 200 brains 400 hours 1 hour

Time for 200 lobe & 250 hours all lobes (Fig3) and 27 regional analysis regions included above

Improved computational capabilities

1 2 3

AD Project: Overview

MGH Segmentation

Multi-Site Data Acquisition

De-identification and upload

SRB UCSD

BWH/MGH

Multi-site DataQueries and

Statistics

0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0CVLT Discriminability Score

1000

2000

3000

4000

5000

6000

Left Hippocampal Volume

BWH/MGH and UCSD Data

HID

HID

Visualization and Scientific Search with

3DSlicer & Query Atlas

1

2

3

4

5

AD Project: Accomplishments

• Data sharing:• Successfully tested Deidentification 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

SASHA Project: Overview

MGH Segmentation

Data DonorSites

De-identificationAnd upload

JHUShape Analysis

of Segmented Structures

SRB

BWHVisualization

Goal: comparison and quantification of structures’

shape and volumetric differences across patient

populations

1

2

3

4

5

UCSDSupercomputing

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 PC TeraGrid 1 comparison ~431 days

60 comparisons simultaneously ~7 days

Morphometry BIRN: Future

Calibration: Expand imaging modalities,correction methods, refine protocols

Analysis & Visualization: continue integration development, improve automaticity, support new imaging modalities

Computational Informatics: database interoperability, support genomics, support new image data, grid enable

Utilization: propagate widespread utilization of infrastructure, add new sites to testbed

Why are we here today?

WE NEED FEEDBACK FROM YOU:

Which developments are useful for your projects?

Which developments are we missing?

How can we build better bridges to your projects?

Challenges Technical

• Integration of disparate sources (data and software)

• Processing and handling of large datasets

• Federation of databases in compliance with HIPAA

• Quality control

• Audit and versioning requirements

• Accessing legacy data

• Project coordination and knowledge distillation

Sociological• Encouraging collaboration

• Intellectual Property issues (data & software sharing)

• Authorship

Metrics for Success

Adopted for use by increasing numbers of experts

Sharing of tools and infrastructure with scientific community

Creation and maintenance of a valuable image archive that supports on-going research

Peer reviewed publications in scientific and technical journals

Presentations at national and international scientific meetings

Professional advancement of key personnel linked to success of project

AD Project: Accomplishments

• Successfully tested DUP to share data across sites following federal HIPAA guidelines

• Established common database schemas for clinical and derived morphometry data at different sites (HID)

• Enabled mediated queries that interrogate databases at two sites

• Successfully tested integration of tools for common analysis and data mining

• MGH Subcortical segmentation completed on UCSD data

• Univariate and bivariate statistical tools through the BIRN Portal and HID Query Interface

• Data visualization and intelligent scientific search based on anatomical labels using 3DSlicer and Query Atlas

MIRIAD Project: Accomplishments

• 50 depressed, 50 controls, imaged at baseline and 2 years• Parietal lobe smaller in depressed (p < 0.02)• In subjects responding to therapy:

• Temporal lobe smaller (p < 0.08)• Frontal lobe was not smaller (p < 0.6)

2004 nih building on the birn bruce rosen, md phd randy gollub, md phd steve pieper, phd ...

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