The Morphometry BIRN (mBIRN) testbed provides tools and best practices to aid researchers across the entire data flow of an experiment, with an emphasis on the particular challenges of multi-site studies. The goal of a brain morphometry experiment is to use magnetic resonance imaging (MRI) to visualize and find quantitative measures for brain structures. The types of MRI included in this domain include conventional structural (T1-weighted) MRI as well as diffusion tensor imaging (DTI).
A standard experimental flow follows in the example question and answer sequence below:
What should my imaging parameters be to ensure that I acquire data that will answer the scientific questions that I am interested in? How can I ensure that all images in my study meet minimum quality standards?
The mBIRN has developed imaging protocols for both T1-weighted and diffusion tensor imaging. These protocols cover scanners from the three major manufacturers (Siemens, GE, and Philips).
Distortion Correction Tools
Do I need to correct my images for distortions, and if so, how?
The presence of image distortions can make it difficult to combine data acquired from multiple sites. One method to reduce these artifacts is to increase the bandwidth of the images and to ensure that the same bandwidth is used for all types of images. In addition, with the advent of better gradients in the latest scanners, distortions can be reduced by consistently placing the head in the center of the magnet for every patient.
Image distortions can arise from variations in the main magnetic field (B0), from imaging gradient non-linearity, and from the eddy currents induced by the application of large diffusion-weighting gradient used in DTI. The mBIRN has developed software tools that can correct the distortions arising from these sources.
Once I’ve acquired data, how can I store, organize and query my data?
The ability to store, organize and query data becomes increasingly important as the size of a study increases or when a study is conducted over a number of sites. mBIRN researchers have developed the eXtensible Neuroimaging Archive Toolkit (XNAT) which is a platform designed to facilitate data management. XNAT includes a secure database that is accessed through a web-based user interface. The database is easily modifiable to accommodate a wide range of data and is written with open-source code to facilitate further customization.
How can I process, in an automated fashion, the large amount of data I have acquired?
The ability to send data through a succession of software programs is critical for the successful analysis of complex images. The LONI Pipeline is a data pipeline manager that handles the processing of images in an easily customizable way.
What tools can I use to perform morphometric analysis on my data? How can I perform group analyses on my data?
The mBIRN has produced a rich set of analysis tools for both morphometric (FreeSurfer) and DTI analysis (MRIStudio).
How can I see the results of my study?
Morphometric data can be visualized using 3D Slicer. The current version (Slicer 3) is open source and has the ability to work together with FreeSurfer. Visualization of DTI data can be done using MRIStudio or Slicer 3.
Data Sharing Tools
How can I share the raw and derived data from this study with others?
There is a public instance of XNAT called XNAT Central that allows users to store and share data. Sharing can be restricted to a group or with no access restrictions. Please note that because of HIPAA regulations you cannot share data or make data public without explicit permission from your Institutional Review Board.