Description: Implement a method to create a population-specific Tractography Bundle Atlas. Given an input of several subjects’ segmented white matter tracts, create one standard atlas of bundles for the population. The method will utilize the streamline-based registration framework available in DIPY.
Steps:
Understand Diffusion Tensor Imaging and Tractography data
Implement Bundle Atlas creation method
Write DIPY workflow of the method
Test it on different data sets
Difficulty: Hard
Skills required: Python, Registration.
Skills preferred: Experience with Diffusion Tensor Imaging
Dear mentors, this is David Romero. I am PhD student in biomedical engineering very much interested on the project.
After going through the Direct Bundle Registration tutorial and [Garifallidis,2015] I have some questions:
For clarification, I read how in [Garifallidis,2015] a probabilistic atlas of the optic radiation is built. The final goal of this project would be to do something similar but with other tracts?
Should the atlas building rely only on segmented tracts or would it be possible to use other inputs (e.g. T1w images) to aid the process?
Should the implementation be based entirely on the SLR? As far as I understand SLR uses an affine registration and it might be interesting to introduce non-linear registration. As a possible idea, some authors in my main research field (retinal imaging) use linear registration to create an initial atlas and then use non-linear registration to refine that atlas on an iterative process (I hope it makes sense, I am not so familiar with bundle registration).
You are on a right track. I agree with you to include non-linear registration after affine registration. Although, the non-linear registration of streamlines is tricky.
, I will think about the overall approach and prepare a draft proposal.
!