Slicer3:BrainLabModule

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Aim

BrainLab (http://www.brainlab.com) has recently introduced a custom designed client/server architecture called VectorVision Link (VV Link), an open API (but not free) to communicate with the IGT environment. This API allows external programs to receive tracker information from the BrainLab system. We propose to create a comprehensive workflow to interface 3D Slicer to the BrainLab system and to use it for research. Specifically, we describe steps for neurosurgeons to use Slicer3 to perform some research in DTI visualization in OR while using BrainLab as the navigation system. Here is the scenario:

The BrainLab system will still run as usual; we won't install any software and hardware on the computer running the BrainLab system and will not affect the FDA status of the BrainLab system. Slicer3 runs on a different computer than the BrainLab system. These two computers need to be connected to each other using a network or router connection. During surgical procedures, the BrainLab sends real-time data from its tracking system and/or images to Slicer3. The information from the Brainlab tracker can be used to manipulate dynamic DTI visualization in Slicer3.

Research Plan

3D Slicer currently provides very basic technology for annotating images. This limits users in their ability to properly capture semantic information contained in images and data sets. We propose to address this issue by expanding Slicer's mark up and annotation capabilities. New features will include:

• a rich set of geometric objects for improved visual differentiation between annotations
• markers for measuring anatomical characteristics, such as the volume of an annotated region, to provide patient specific information difficult to extract from visual inspection
• entry fields beyond free-text, such as graphics and external data, to capture comprehensive information and support for emerging domain specific ontologiesand
• a full integration of these capabilities with the mrml tree to support Scenesnapshots, load, save both to disk and XNAT.

We will implement these features by developing two different modules. The first module, called Marker Module, creates different types of markers based on current ITK technology. The user defines the appearance of the marker by specifying its color, size, and shape, such as points and 3D boxes. The user also labels each marker with tags and specifies its function, such as measuring the volume of a region.

The Annotation Module, the second module, provides the interface for annotating images with these markers. Users place the markers on the image and further specify the semantic information through free text, plots, and references to ontology and internet. The annotations are shown both in 3D and 2D viewers. The module also allows annotating entire scenes by linking annotations across images, as well as within an image. All annotations are stored in a database targeted towards medical imaging, called XNAT. The structure of the database is automatically defined by the tags of the markers. Thus, users can query across large image data sets by looking for specific tag values.

Both modules are accompanied by training materials and documentation to ensure usability.

Key Personnel

Haiying Liu
Noby Hata
Ron Kikinnis

Progress

• 03/29/10
  • Ron, Noby and Haiying met at Ron's office to discuss the specs of the module and time frame for implementation.

Dependency

The following modules are required for BrainLab module to work properly:
Fiducial
OpenIGTLink
DTMRI
FiducialSeeding