期刊
MEDICAL IMAGE ANALYSIS
卷 39, 期 -, 页码 162-177出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.media.2017.03.007
关键词
Diffusion MRI; Brain sheet structure; Normalized convolution; Lie bracket
类别
资金
- Physical Sciences division of the Netherlands Organization for Scientific Research (NWO) [612.001.104]
- VIDI Grant from NWO [639.072.411, 617.001.202]
- NIH [R01MH074794, P41EB015902, P41EB015898]
- Foundation for Fundamental Research on Matter (FOM)
- Netherlands Organisation for Scientific Research (NWO)
- 16 NIH Institutes and Centers
- NIH Blueprint for Neuroscience Research
- McDonnell Center for Systems Neuroscience at Washington University
- National Institute of Dental and Craniofacial Research (NIDCR)
- National Institute of Mental Health (NIMH)
- National Institute of Neurological Disorders and Stroke (NINDS)
- University Fund Eindhoven
- [NIH/1S10 ODOD010683-01]
The hypothesis that brain pathways form 2D sheet-like structures layered in 3D as pages of a book has been a topic of debate in the recent literature. This hypothesis was mainly supported by a qualitative evaluation of path neighborhoods reconstructed with diffusion MRI (dMRI) tractography. Notwithstanding the potentially important implications of the sheet structure hypothesis for our understanding of brain structure and development, it is still considered controversial by many for lack of quantitative analysis. A means to quantify sheet structure is therefore necessary to reliably investigate its occurrence in the brain. Previous work has proposed the Lie bracket as a quantitative indicator of sheet structure, which could be computed by reconstructing path neighborhoods from the peak orientations of dMRI orientation density functions. Robust estimation of the Lie bracket, however, is challenging due to high noise levels and missing peak orientations. We propose a novel method to estimate the Lie bracket that does not involve the reconstruction of path neighborhoods with tractography. This method requires the computation of derivatives of the fiber peak orientations, for which we adopt an approach called normalized convolution. With simulations and experimental data we show that the new approach is more robust with respect to missing peaks and noise. We also demonstrate that the method is able to quantify to what extent sheet structure is supported for dMRI data of different species, acquired with different scanners, diffusion weightings, dMRI sampling schemes, and spatial resolutions. The proposed method can also be used with directional data derived from other techniques than dMRI, which will facilitate further validation of the existence of sheet structure. (C) 2017 Elsevier B.V. All rights reserved.
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