Neuroimage Computing
Department of Computer Science and Software Engineering
Assignment 4: Quantitative imaging and tractography
1.Brain tractography with 3D Slicer (50 points)
Brain tractography is an important tool in neuroimage analysis and image-guided neurosurgery. In
class, we have learnt about the basics of deterministic tractography. In this section, you will follow
the 3D Slicer DMRI module tutorial for generating the tractography for the corpus callosum.
The complete tutorial and the related data package can be downloaded below:
https://spujol.github.io/SlicerDiffusionMRITutorial/
Please provide screenshots for the following key images that demonstrate successful completion
of the sub-tasks: brain mask, FA map overlaid with color-coded DTI scan, 3D visualization of
tensors with Glyphs, and final tractography at different steps.
Bonus Question: R2* mapping (25 points, worth half of a regular assignment)
In the “Resource” folder, you will find the folder “Multi-echo GRE”, which contains four coregistered
MRI scans (e.g., 4echo-GRE-1.nii.gz) obtained from a multi-echo GRE sequence with
a 7T MRI. The protocols for these images only differ in terms of the echo time (TE), and the higher
their serial number, the higher the TE value. The specific imaging parameters are as follows:
TE=2.53/7.03/12.55/20.35 ms, TR=33 ms, α=11°
We know that for a GRE MRI sequence, the signal equation is
As the α and TR remain the same at each spatial location of the scan, the MRI signal for the scans
can be written as:
Using a linear regression model for these four MRI scans
a. Please derive the formula for finding the R2* (1/T2*) value at each voxel location (Hint:
you can take a log of the equation and use the least squares approach:
https://www.youtube.com/watch?v=P8hT5nDai6A)
b. Use either “fslmaths” or MATLAB, please generate the R2* map for this subject. Provide
a screenshot of the result and comment on the obtained R2* map in terms of the intensity
uniformity of the values.
For more information regarding the data, please refer to the following publication:
Gulban, O. F., Schneider, M., Marquardt, I., Haast, R. A. M., & De Martino, F. (2018). A scalable method to
improve gray matter segmentation at ultra high field MRI. PLOS ONE, 13(6), e0198335.
