Abstract

Current tractography methods generate tractograms composed of millions of 3D polylines, called fibers, making visualization and interpretation extremely challenging, thus complexifying the use of this technique in a clinical environment. We propose to progressively simplify tractograms by grouping similar fibers into generalized cylinders. This produces a fine-grained multi-resolution model that provides a progressive and real-time navigation through different levels of detail. This model preserves the overall structure of the tractogram and can be adapted to different measures of similarity. We also provide an efficient implementation of the method based on a Delaunay tetrahedralization. We illustrate our method using the Human Connectome Project dataset.

Video

Downloads

• Manuscript: PDF
• Source code: coming soon on github

Bibtex

@inproceedings{MGRCBTI:2018:PBT,
 author = {Corentin Mercier and Pietro Gori and Damien Rohmer and Marie-Paule Cani and Tamy Boubekeur and Jean-Marc Thiery and Isabelle Bloch},
 title = {Progressive and Efficient Multi-Resolution Representations for Brain Tractograms},
 booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine (EG VCBM)},
 year = {2018}
}