Tractography (DICOM TR)

Results section

Overview

The purpose of this task is to demonstrate support of the DICOM Tractography Results (DICOM TR) object.

The basic read task involves loading the existing DICOM TR object, and demonstrating visualization of the tractography relative to the reference image.

The write task involves generation of tractography streamlines from the provided Diffusion Weighted Image DICOM files and storing the result as a DICOM TR object. Note that this task does not seek to compare tractography algorithms, evaluate medical or neuroanatomic accuracy or usefulness of results, etc. The only question of interest is interchange of DICOM TR objects between systems.

Tasks for participants

  1. Description of the platform/product:

    • name and version of the software used for testing
    • free? if yes - include the download link
    • commercial? if yes - include the home page for the product
    • open source? if yes - provide a link to source code
    • what DICOM library do you use? - if you use certain DICOM toolkit to support this functionality, please list it, if possible

    • Description of the relevant features of the platform:

      • are multiple tracksets supported in a single file?
      • do you support any optional measurement data associated with a track?
      • do you support any optional summary statistics associated with a track set?
      • do you write any other optional information to the TR file? (e.g. acquisition, model, attribute, algorithm identification etc.)
  2. Read task (for each dataset!)

    • load each of the DICOM TR datasets that accompany the imaging series into your platform
    • submit the following screenshots:
      • demonstrate the 2D overlay of the track sets on the B0 series (if possible)
      • demonstrate several 3D perspectives (if possible)
      • demonstrate any other components of the user interface (e.g., presentation of the associated measurements, communication of the algorithm metadata).
  3. Write tasks

    • Single trackset: select a tractography streamline bundle using any method available in your platform.
    • Multiple tracksets: select any two streamline bundles using any method available in your platform (non-intersecting). Make sure to create separate trackset for each of the segmented areas!
    • save the results as DICOM TR; if possible, please include in the series description the name of your tool to simplify comparison tasks!
    • as part of quick checks, confirm that the resulting TR object has the same FrameOfReferenceUID as the reference image
    • UPLOAD THE RESULTING DICOM TR FILES HERE. (note: after upload, data will be publicly accessible from link in results section)

Note: (1) there is no evaluation of accuracy or usefulness of tractography streamlines, any method is good; (2) the screenshots and the DICOM TR objects you submit will be distributed publicly and included in this document in the Results section.

Diffusion Weighted Image Source Datasets

DOWNLOAD ALL DWI DATASETS (zip file).

DWI dataset #1

The imaging dataset is a diffusion weighted image of a human brain acquired on a Siemens Verio (1 B0 and 6 gradient directions; stock sequence; mosaic OFF; CSA header private tags preserved during anonymization). Data credit: O'Donnell Group, Laboratory of Mathematics in Imaging, Brigham & Women's Hospital.

DWI dataset #2

The imaging dataset is a diffusion weighted image of a human brain acquired on a Siemens Trio (mosaic off; no CSA header present). Data credit: University of Iowa BRAINS project, DWIConvert test dataset. (source, metadata, and license)

DWI dataset #3

The imaging dataset is a diffusion weighted image of a human brain acquired on a 3T GE Signa HDx. Data credit: University of Iowa BRAINS project, DWIConvert test dataset (source, metadata, and license).

Generated Tractography Result datasets

Download the DICOM TR datasets produced by the platforms that already submitted results (data is organized in subfolders corresponding to the individual platforms):

LINK TO RESULTS DOWNLOAD FOLDER

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