[PROJECT] Transform tractography data to HiP-CT space

[kabilar]

Goal

• The overarching goal is to integrate micro and meso datasets from the same brain. For this project specifically, we are looking to transform a tractography dataset to HiP-CT space, and visualize the streamlines in Neuroglancer.

Developers will be provided with the following datasets:

1. HiP-CT (dandiset 26, sub-I58)
2. Diffusion MRI (NIfTI file format)
3. Tractography streamlines (trk file format) which are in diffusion space
4. Affine transformation between the diffusion MRI and HiP-CT spaces

[neurolabusc]

@frheault you already have Python scripts to read/write/convert streamline formats. It seems like these could be combined with dipy registration functions to provide a robust solution to this question.

[ayendiki]

To clarify, the focus of this project is on getting multi-scale datasets onto DANDI and into neuroglancer. But, for anyone who's interested, I'm also happy to discuss our approach to cross-scale, cross-modal registration that we proposed in our UM1 (and I'm eager to hear from the UMinn UM1 on what they proposed for this purpose). It's bit hard to convey the problem statement in a short project description, so best to wait till Monday.

[balbasty]

https://github.com/balbasty/ngtracts/blob/main/notebooks/show_tract.ipynb

Modality	Intensity	+ Tracts
MRI
HiP-CT

And the 3D tracts

[kabilar]

cc @MikeSchutzman

[neurolabusc]

@kabilar this is timely, the NiiVue team has recently added support for additional formats (TT, TSF), TRX groups and have added the option to extrude streamlines as cylinders which can use custom shaders. You can try out the live demo. Feel free to adapt any of these to neuroglancer.

[kabilar]

Thanks team. Closing this issue as we have some prototypes of this solution and are preparing for the upcoming hackathon.