These are answers to the diffusion related 'Study Questions'.

Diffusion Analysis

What happens if you try to run dt_recon more than one time?

You will receive and error that multiple volumes are found and the program will stop. This command can be run a second time after deleting the existing output directory and using a clean copy of the data.

What does dt_recon do?

This command accomplishes preprocessing of the data by correcting for eddy currents and motion artifacts and generates multiple NIFTI volumes.

What step should be completed after running dt_recon?

As always, you should check the registration to ensure that dt_recon was accurate in correcting for motion and eddy currents.

What needs to be done in order to analyze specific subcortical and white matter regions in the diffusion data?

The subcortical segmentations need to be resampled into the diffusion space using the ‘mri_vol2vol’ command

When would you need to add padding to the mask that was created for you?

If there are any problems in the mask, you can improve the accuracy of the map by adding padding to the mask. You can increase the boundary of the mask by one voxel in every direction using the mri_mask --bb flag.


What are the three steps involved in processing individual subjects with TRACULA and what command runs them?

The three steps are pre-processing, FSL’s bedpostX, and reconstructing white-matter pathways. These three steps are run using trac-all.

What is the configuration file? What happens if you run TRACULA without a configuration file?

A Unix shell script where you set variables to specify the location of the input data and variuos processing preferences. If you run TRACULA without a configuration file, you will only be able to use the default processing options.

Which model of diffusion does TRACULA use to reconstruct pathways from the DWI data?

The ball-and-stick model.

What is diffusion MRI particularly sensitive to and what does this result in? How does TRACULA assess this error?

Diffusion MRI is sensitive to head motion which results in a misalignment between consecutive DWIs and alters the intensities of the DWIs. TRACULA assess head motion through four measures during the quality assessment step. These measures include: the average volume-by-volume translation, the average volume-by-volume rotation, the percent of slices with excessive intensity drop-out, and the average drop-out score for slices with excessive intensity drop-out.

FsTutorial/DiffusionStudyQuestions (last edited 2018-02-27 13:15:27 by MorganFogarty)