Probabilistic tractography for topographically organized connectomes

Dogu Baran Aydogan*, Yonggang Shi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

10 Citations (Scopus)


While tractography is widely used in brain imaging research,its quantitative validation is highly difficult. Many fiber systems,however,have well-known topographic organization which can even be quantitatively mapped such as the retinotopy of visual pathway. Motivated by this previously untapped anatomical knowledge,we develop a novel tractography method that preserves both topographic and geometric regularity of fiber systems. For topographic preservation,we propose a novel likelihood function that tests the match between parallel curves and fiber orientation distributions. For geometric regularity,we use Gaussian distributions of Frenet-Serret frames. Taken together,we develop a Bayesian framework for generating highly organized tracks that accurately follow neuroanatomy. Using multi-shell diffusion images of 56 subjects from Human Connectome Project,we compare our method with algorithms from MRtrix. By applying regression analysis between retinotopic eccentricity and tracks,we quantitatively demonstrate that our method achieves superior performance in preserving the retinotopic organization of optic radiation.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783319467191
Publication statusPublished - 1 Jan 2016
MoE publication typeA4 Article in a conference publication
EventInternational Workshop on Simulation and Synthesis in Medical Imaging - Athens, Greece
Duration: 21 Oct 201621 Oct 2016
Conference number: 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9900 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


WorkshopInternational Workshop on Simulation and Synthesis in Medical Imaging
Abbreviated titleSASHIMI


  • Bayesian inference
  • Probabilistic tractography
  • Visual pathway


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