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

2004 All Hands Meeting

Morphometry BIRNBruce 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

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



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

Tool Site

• Freesurfer MGH

• Slicer BWH

• LONI Pipeline UCLA

• LDDMM Johns Hopkins

• Query Interface UCSD


Shared Data

Data Site

AD BWH/MGH

AD UCSD

AD WashU

Depression Duke

MCI UCI

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



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


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: Overview

N=200

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



• 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



AD Project: Overview

MGH Segmentation

Multi-Site Data Acquisition

De-identification and upload

SRB UCSDN=125

BWH/MGHN=118

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

Le

ft H

ipp

oca

mp

al V

ol u

me

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 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



• 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



SASHA Project: Overview

MGH Segmentation

Data DonorSite (WashU)

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

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 PC TeraGrid 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 • B0 inhomogeneities• B1 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

B0 inhomogeneitiesOn-line motion correction

B1 inhomogeneities

T2-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 FLAIRRaw Data

Segmentations

CSF

WM

GMVascular 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]

• B0 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

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 directionsBaseline 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

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

OntologyHaystack / Semantic Web

RPDR

Clinical Measures

Genotype

Local Storage

BIRN Rack

SRBMCAT

HID

DU

P

Calibration & Analysis

Tools

GRID

PortalMediator

Institution A

BIRN Rack

SRBMCAT

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.)


StandardizedAcquisition

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


Single subject data: view, browse, download


mBIRN Ontologies

Brain

Cerebellum Cerebrum

Cerebral white matter …

Frontal cortex Temporal cortex

Superior temporal Mesial temporal

Amygdala Hippocampus

…

Cerebral cortex

…

…

…

Neuroanatomical ontology

Brain

Cerebellum Cerebrum





…

Cerebral cortex

…

…

…

Brain

Cerebellum Cerebrum





…

Cerebral cortex

…

…

…

Neuroanatomical ontology

MMSE

Cognitiveimpairment

Dementiaseverity

Cognition

Assessment

Neuropsychology

Alzheimer’s

Task and score description

MMSE

Cognitiveimpairment

Dementiaseverity

Cognition

Assessment

Neuropsychology

Alzheimer’s

Task and score description

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

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

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