Parallel Transport Tractography

Tractography is an important technique that allows the in vivo reconstruction of structural connections in the brain using diffusion MRI. Although tracking algorithms have improved during the last two decades, results of validation studies and international challenges warn about the reliability of t...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on medical imaging 2021-02, Vol.40 (2), p.635-647
Main Authors: Aydogan, Dogu Baran, Shi, Yonggang
Format: Article
Language:English
Subjects:
Algorithms
Anatomy
Brain
Brain - diagnostic imaging
Brain architecture
Brain modeling
Complexity theory
Connectome
Diffusion Magnetic Resonance Imaging
diffusion MRI
Diffusion Tensor Imaging
Humans
Image color analysis
In vivo
In vivo methods and tests
Magnetic resonance imaging
National Institutes of Health
Noise propagation
Noise reduction
parallel transport
Probabilistic logic
Propagation
Reproducibility of Results
Tracking
tractography
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Tractography is an important technique that allows the in vivo reconstruction of structural connections in the brain using diffusion MRI. Although tracking algorithms have improved during the last two decades, results of validation studies and international challenges warn about the reliability of tractography and point out the need for improved algorithms. In propagation-based tracking, connections have traditionally been modeled as piece-wise linear segments. In this work, we propose a novel propagation-based tracker that is capable of generating geometrically smooth ( {C}^{{1}} ) curves using parallel transport frames. Notably, our approach does not increase the complexity of the propagation problem that remains two-dimensional. Moreover, our tracker has a novel mechanism to reduce noise related propagation errors by incorporating topographic regularity of connections, a neuroanatomic property of many brain pathways. We ran extensive experiments and compared our approach against deterministic and other probabilistic algorithms. Our experiments on FiberCup and ISMRM 2015 challenge datasets as well as on 56 subjects of the Human Connectome Project show highly promising results both visually and quantitatively. Open-source implementations of the algorithm are shared publicly.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2020.3034038