BundleAtlasing: unbiased population-specific atlasing of bundles in streamline space

  1. Romero-Bascones, David 1
  2. Ayala, Unai 1
  3. Barrenechea, Maitane 1
  4. Chandio, Bramsh Qamar 2
  5. Fadnavis, Shreyas 2
  6. Park, Jong Sung 2
  7. Koudoro, Serge 2
  8. Garyfallidis, Eleftherios 2
  1. 1 Biomedical Engineering Department, Mondragon Unibertsitatea, Mondragón, Spain
  2. 2 Department of Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, IN, United States
Actes de conférence:
Proceedings of the Annual Meeting of the International Society for Magnetic Resonance in Medicine

Année de publication: 2022

Type: Communication dans un congrès

GOOGLE SCHOLAR lock_openeBiltegia editor

Résumé

White matter bundle atlases play a crucial role in the segmentation of bundles and the understanding of brain connectomes. However, the construction of streamline atlases that accurately represent the underlying population anatomy is challenging. In this work, we present BundleAtlasing, a new method to compute population-specific bundle atlases in the space of streamlines. The proposed approach is based on two key aspects: an iterative groupwise unbiased bundle registration, and a pairwise bundle combination strategy. We show that our method is able to correctly generate unbiased atlases that represent the average group anatomy of a population.

€ Voir financement

Information sur le financement

This research has received funding from the 2021 Google Summer of Code program.

Financeurs