Informatics for the Neuroimaging Research

EnterpriseDan Marcus

Washington UniversityNITRC Enhancement Grantee Meeting

Monday, June 30, 2008

The Central Neuroimaging Data Archive

• Supporting Wash U investigators since 2003• Currently holds 25000 MR, PET and CT scans

from over 5000 individual studies• ~100 active users from two dozen labs• Supports all of the Univ.’s imaging facilities

and many of its research centers.

Defining the enterprise

Lab

Stakeholders: Principal investigator, students, postdocs, research techs.

Defining the enterprise

Center

LabLab Lab

Stakeholders: Director, scanner facility, IT department, human studies

Defining the enterprise

Center

LabLab Lab

Center

LabLab Lab

Center

LabLab Lab

Multisite collaborationMultisite collaboration

Stakeholders: study PI, individual PIs, research cores, coordinating center

Defining the enterprise

• Labs: Focused on data & analysis• Centers: Focused on operations & oversight• Multisite studies: Focused on technical &

scientific coordination and logistics

Defining informatics: Data Capture

NEUROIMAGING

GENETICS

OTHER SOURCES

Integrity: Do I have the data?Quality Control: Are the data any good?

Defining informatics: Local Use

NEUROIMAGING

GENETICS

OTHER SOURCES

Application: Can I do things with the data?Automation: Am I optimizing throughput?

Defining informatics: Collaboration

NEUROIMAGING

GENETICS

OTHER SOURCES

Access: Are colleagues getting the data they need?Security: Are colleagues getting data they shouldn’t?

Defining informatics: Public access

NEUROIMAGING

GENETICS

OTHER SOURCES

Privacy: Am I respecting the rights of the study participants?Convenience: How usable are the data?

QUARANTINE LOCAL USE COLLABORATION PUBLIC ACCESSCAPTURE

NEUROIMAGING

GENETICS

OTHER SOURCES

The XNAT workflow

• Quality control• Data archiving• Data access• Security

• Visualization• Automation• Integration• Data sharing

Lessons learned: stakeholders

• Identify the stakeholders and their personalities– The Micromanager– The Empire builder– The Outsourcer– The Benefactor

• N investigators ≠N databases

Lessons learned: budgets

• Hardware costs will be over budgeted.• Personnel costs will be under budgeted.

Lessons learned: personnel

• Hire software engineers.• Good Java programmers are rare.• Good Java programmers who will work for

what you want to pay them? Forget about it.• There are no rules.• Except: your software engineering team is

your most important asset.

Lessons learned: software engineering

• Use the least possible technology.• Half the features. Twice the usability.• Compliance issues (HIPAA, IRBs, IT security)

becomes increasingly burdensome• Open source is your friend.

Lessons learned: data

• Remain agnostic to formats• Except DICOM. Drink the Kool-Aid.