CTSC Ellen Grant, CHB

Back to CTSC Imaging Informatics Initiative

CTSC Informatics Goals

The use-case can be broken into two distinct steps, including Data Management, and Processing.

• Step I: Data Management Describe and upload retrospective datasets (roughly 1 terabyte) onto the CHB XNAT instance, and confirm that a specific tractography processing workflow is supported by the XNAT Data Model and web services API. The worklfow involves
  • making specific queries (see below for details),
  • data download conforming to specific naming convention and directory structure
  • generating and uploading specific results ( see below for details).
• Step II: Data Processing Implement & execute the script-driven tractography workflow

Success Metrics

Step 1: Data Management

• Visual confirmation (via web GUI) that all data is present and organized appropriately
• Successful tests that responses to XNAT queries for all MRIDs given a protocol name match results returned from currently-used search on the local filesystem.
• Query/Response should be efficient
• other?

Step 2: Data Processing

• Pipeline executes correctly
• Pipeline execution not substantially longer than when all data is housed locally
• other?

Participants

• sites involved: MGH NMR center, MGH Radiology, CHB Radiology
• number of users: ~10
• PI: Ellen Grant
• staff: Rudolph Pienaar
• clinicians
• IT staff

Data

Retrospective data consists of ~1787 studies, ~1TB total. Data consists of

• MR data, DICOM format
• Subsequent processsing generates ".trk" files
• ascii text files ".txt"
• files that contain protocol information

Current Workflow

Current Data Management Process

(schematic to come...)

DICOM raw images are produced at radiology PACS at MGH, and are manually pushed to the PACS hosted on KAOS resided at MGH NMR center. The images are processed by a set of PERL scripts to be renamed and re-organized into a file structure where all images for a study are saved into a directory named for the study.

DICOM images are currently viewed with Osiris on Macs.

Data Management need

It is desired to develop an Image Management System for BDC (IMS4BDC) with which at least the following can be done:

• Move images from MGH (KAOS) to a BDC machine at Children’s
• Import legacy data into IMS4BDC from existing file structure and CDs
• Query capabilities: (example) “Show all diffusion studies where patients ages are < 6”
• Mange post-processed images
• Sharing images with clinical physicians; export post-processed data back to clinical PACS

Other Information

Rudolph has worked with XNAT support group at Harvard.