DTI:Acquisition

Definition of DTI Protocols

Temporary page containing content from the Morphometry BIRN Best Practices DTI page that is being updated.

Work is ongoing within the BIRN project to define DTI acquisition protocols that can be used in single and multi-site trials. This work is being informed by the DTI calibration studies performed by the JHU group.

Basic Steps for Processing DTI data

1) If required, convert data from the original format to another file format that your software accepts. Some common ones are:

• Digital Imaging and Communications in Medicine (DICOM) - all scanners that I know of support DICOM, but one must be aware that there are variations in the way that DWI information is stored and important information may be stored in the vendors private fields. The relevant fields for DWI are:
  • 0018 9075 CS 1 Diffusion Directionality
  • 0018 9076 SQ 1 Diffusion Gradient Direction Sequence
  • 0018 9087 FD 1 Diffusion b-value
  • 0018 9089 FD 3 Diffusion Gradient Orientation
  • 0018 9117 SQ 1 MR Diffusion Sequence
  • 0018 9147 CS 1 Diffusion Anisotropy Type

Note that while there are tags for the gradient orientation and b-value there is not a tag for the entire b matrix. Also notice that the vendor-specific measurement frame is also not stored - essentially you need to know the relationship between the coordinate frame in which the gradient directions are defined and the coordinate frame in which the image orientation is defined or your color maps will not be correct. Since, one usually doesn't know these coordinate frames, one must resort to flipping the sign of various gradient direction components until the directions given by the tensor processing correspond to what you know from the anatomy. The NA-MIC group has also noted that GE has stored the gradient directions, not in the tag assigned to that quantity, but in other fields. Siemens (as of 2006) stores the b-matrix as well as other diffusion information.

See here for more information about the format of DTI data as it is acquired by each of the dominant MR scan manufacturers. Learn more about DICOM here.

• Nearly Raw Raster Data (NRRD) - see teem.sourceforge.net/nrrd/format.html
• Neuroimaging Informatics Technology Initiative (NIfTI, nifti.nimh.nih.gov/) - this format has a 352 byte header
• Raw (no header) - DTIStudio allows the user to input all the file parameters using a .dpf (diffusion parameters file) file
• Analyze - This format uses either separate files for the header and images or an image file with a header. The extension is .hdr / .img The .img file contains the images. ( www.grahamwideman.com/gw/brain/analyze/formatdoc.htm )
• PAR/REC - this is a Philips format. The .PAR file contains the parameters for the scans and the .REC file contains the data.
• p-file - this is a GE format, which contains the images of interest (and possibly other files). The data is stored in the /usr/g/mrraw directory on the scanner and have the format PXXXXXX.X

2) If necessary for your processing application, make files that contain the b-values and b-vectors for your data. Determine whether the b=0 scans come at the beginning or the end of the data file. Are there any extra frames included in the volume, i.e., some manufacturers include an 'isotropic-weighted' scan at the end of the volume (Philips does this, see below)? If so, you should remove these files before you convert the original files to the format you will be working with.

3) Perform self-registration of the data set. With structural/T1-weighted data you could do rigid alignment of the data(6 degree-of-freedom {DOF} registration), but with DTI data it is better to do a 12 DOF registration since each unique diffusion-weighted direction (DWD) results in different image distortions. There are existing tools available to correction for these distortions, for example, the tool here is a BIRN developed tool.

4) Check the image volume for bad datasets. "Bad" data usually arises from subject motion. Given that these scans are diffusion sensitized, subject motion can affect the signal intensity in an image. If the motion is large enough image intensity dropout can occur. Some tensor calculation packages allow you to remove from the calculation data that is thought to be bad (DTIStudio allows you to do this, for example). If your tensor calculation package does not have this feature, lobby the code developers to include it!

5) Calculate the tensors and associated metrics (FA, eignevalues, eigenvectors, etc.) using your processing package.

6) Use Brain Extraction Tool (BET, University of Oxford FMRIB Software Library, www.fmrib.ox.ac.uk/analysis/research/bet/ ) or some other method to get rid of the noise pixels around the brain. Some packages use a simple erosion-based algorithm. Personally, I use BET to get rid of most of the unwanted pixels and then my own code to clean up the bit that BET leaves behind. This is most important if you are doing something like whole-brain histograms of FA, but not important at all if you are doing ROI analysis.

7) Analyze the tensor metric data. At this point you can construct color maps from the 1st eigenvalue and FA maps, perform fiber tracking, etc.

General References for DTI

1. J. A. Farrell, B. A. Landman, C. K. Jones, S. A. Smith, J. L. Prince, P. C. van Zijl, and S. Mori. "Effects of SNR on the Accuracy and Reproducibility of DTI-derived Fractional Anisotropy, Mean Diffusivity, and Principal Eigenvector Measurements at 1.5T", Journal of Magnetic Resonance Imaging. 26(3): 756-767. August 2007.
2. B. A. Landman, J. A. Farrell, C. K. Jones, S. A. Smith, J. L. Prince, P. C. van Zijl, and S. Mori. "Effects of Diffusion Weighting Schemes on the Reproducibility of DTI-derived Fractional Anisotropy, Mean Diffusivity, and Principal Eigenvector Measurements at 1.5T", NeuroImage. 36(4): 1123-1138. July 2007.