Anatomically informed multi-level fiber tractography for targeted virtual dissection

Andrey Zhylka (Corresponding author), Alexander L.G. Leemans, Josien P.W. Pluim, Alberto de Luca

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
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Abstract

Objectives: Diffusion-weighted MRI can assist preoperative planning by reconstructing the trajectory of eloquent fiber pathways, such as the corticospinal tract (CST). However, accurate reconstruction of the full extent of the CST remains challenging with existing tractography methods. We suggest a novel tractography algorithm exploiting unused fiber orientations to produce more complete and reliable results. Methods: Our novel approach, referred to as multi-level fiber tractography (MLFT), reconstructs fiber pathways by progressively considering previously unused fiber orientations at multiple levels of tract propagation. Anatomical priors are used to minimize the number of false-positive pathways. The MLFT method was evaluated on synthetic data and in vivo data by reconstructing the CST while compared to conventional tractography approaches. Results: The radial extent of MLFT reconstructions is comparable to that of probabilistic reconstruction: p= 0.21 for the left and p= 0.53 for the right hemisphere according to Wilcoxon test, while achieving significantly higher topography preservation compared to probabilistic tractography: p< 0.01. Discussion: MLFT provides a novel way to reconstruct fiber pathways by adding the capability of including branching pathways in fiber tractography. Thanks to its robustness, feasible reconstruction extent and topography preservation, our approach may assist in clinical practice as well as in virtual dissection studies.

Original languageEnglish
Pages (from-to)79-93
Number of pages15
JournalMagnetic Resonance Materials in Physics, Biology and Medicine
Volume36
Issue number1
DOIs
Publication statusPublished - Feb 2023

Funding

FundersFunder number
National Institutes of Health
NIH Blueprint for Neuroscience Research
McDonnell Center for Systems Neuroscience
European Union 's Horizon 2020 - Research and Innovation Framework Programme
Horizon 2020765148, 1U54MH091657

    Keywords

    • Corticospinal tract
    • Diffusion MRI
    • White matter
    • Algorithms
    • Pyramidal Tracts/diagnostic imaging
    • Image Processing, Computer-Assisted/methods
    • Diffusion Magnetic Resonance Imaging/methods
    • Diffusion Tensor Imaging/methods

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