Elsevier

NeuroImage

Volume 169, 1 April 2018, Pages 524-539
NeuroImage

Surface-enhanced tractography (SET)

https://doi.org/10.1016/j.neuroimage.2017.12.036Get rights and content

Highlights

  • Novel surface seeding and stopping strategies using the high-resolution T1 image.

  • Novel differential geometry modeling of the white matter fibers under the cortex.

  • Surface-enhanced tractography algorithm reduces the gyral and length bias.

  • SET improves reproducibility of tractograms and connectivity matrices.

Abstract

In this work, we exploit the T1 weighted image in conjunction with cortical surface boundary to improve the precision of tractography under the cortex. We show that utilizing the cortical interface and a surface flow, to model the superficial white matter streamlines, enhance and improve tractography trajectory near the cortex. Our novel surface-enhanced tractography reduces the gyral bias, the length bias and the amount of false positive streamlines produced by tractography. This method improves the reproducibility and the cortical surface coverage of tractograms which are crucial for connectomics studies. The usage of cortical surfaces, extracted from the standardly acquired 1 mm isotropic T1, is a straightforward and effective way to improve existing tractography processing pipelines and structural connectivity studies.

Introduction

Diffusion MRI (dMRI) is a non-invasive technique that allows the reconstruction of the white matter (WM) structure. Axonal pathways can be reconstructed in-vivo by following the local orientation of the water diffusion with a process called tractography. This in-vivo reconstruction of the white matter can be used for structural connectivity studies (Wakana et al., 2007, Hagmann et al., 2007, Fornito et al., 2013). Structural connectivity mapping (connectomics) can be estimated through streamline endpoints produced by tractography (Yo et al., 2009, Jbabdi et al., 2015).

However, limitations in dMRI and tractography can lead to biased measurements and conclusions (Jones, 2008, Descoteaux et al., 2009, Yo et al., 2009, Jones and Cercignani, 2010, Jbabdi and Johansen-Berg, 2011, Tournier et al., 2011, Jones et al., 2013). Compared to standard anatomical MRI image, dMRI has an intrinsically low signal-to-noise ratio and low spatial resolution (Tournier et al., 2011). Partial volume effect (PVE), caused by the poor spatial resolution (typically 2 mm isotropic), and angular discretization reduces the precision of streamlines tractography. Another problem is that most streamlines stop in the middle of the white matter without reaching the gray matter (GM) regions (seeding and masking problems) (Jbabdi and Johansen-Berg, 2011, Smith et al., 2012b, Girard et al., 2014). Moreover, the low resolution of dMRI can also lead to the gyral bias of tractography (Van Essen et al., 2013a, Reveley et al., 2015), as shown in Fig. 1, where current iterative tractography algorithms are unable to capture the fanning structure in the gyral blade.

The goal of this work is to confront dMRI limitations in the superficial WM and improve tractography methods to overcome the poor resolution, the partial volume effect and the gyral bias. We therefore propose a surface-enhanced tractography technique, a surface-based differential geometry approach, to model the main orientation of the WM structure under the cortex, without the need of any diffusion information. We also present a surface seeding and stopping strategy, utilizing the cortex normal and curvature to improve and enhance current tractography algorithms. Together, these methods lead to more reproducible and less variable tractograms and connectivity matrices.

Section snippets

Background

We now present some of the existing literature on cortical folding models along with the underlying axonal structure. The proposed surface-enhanced method is derived from these gyrification and fiber models.

Methods

In this work, we propose to apply a geometric flow to the WM-GM boundary surface to model the predominant fiber pathway and improve the tractography near the cortex. As specified in the next section, each point of the mesh will flow inwardly, orthogonal to the cortical surface, creating a less convoluted surface. Consequently, this surface flow will be used to initialize and terminate a traditional streamlines tractography algorithm using the dMRI information from the fiber orientation

Human Connectome Project (HCP) data

Surface Flow From Fig. 2, the surface flow evolution and the points moving along the inward normal direction and mean-curvature can be observed. Surface flow can be seen as a smoothing weighted by positive curvature (stiffness) and area (mass) (Fig. 2-a). From each vertex composing the mesh we can reconstruct a line going towards the inside of the gyri, in the surface normal direction, bending with the curvature and area compression (Fig. 2-b). For SET, the inner surface generated from the

Discussion

We have proposed a novel surface-enhanced tractography strategy to overcome the gyral bias of classical tractography algorithms. We showed that tractography can be improved using a surface flow via the WM-GM surface mesh obtained from a high-resolution T1, seeding from the endpoints of this flow and using the flow to back-project streamlines to the WM-GM, subcortical and brainstem surface meshes. Hence, dMRI tractography is improved by our surface-based approach because:

  • it improves the

Conclusion

Surface meshes from a classical T1-weighted image can boost the resolution and performance of dMRI tractography. Since an anatomical T1 image is generally acquired with dMRI and it is often 1 mm isotropic, this is a straightforward and elegant way to improve existing tractography processing pipelines and structural connectivity studies.

A cortical surface flow model of the superficial white matter streamlines can enhance and improve tractography. Surface-enhanced tractography is able to reduce

Conflicts of interest

We have no conflict of interest to declare.

Acknowledgements

A Special thanks to Eleftherios Garyfallidis, François Rheault, Giorgio Innocenti, Jasmeen Sidhu and Tim Dyrby for their help and insights. Acknowledgements to Maxime Chamberland, Kevin Whittingstall and the Sherbrooke Molecular Imaging Center for the data acquisition of the test-retest dataset. For research founding, thanks to FRQNT, NSERC Discovery Grant (RGPIN-2015-05 29 7), CREATE-MIA program (150682827) and Université de Sherbrooke Institutional Research Chair in NeuroInformatics.

References (66)

  • M. Kleinnijenhuis et al.

    Layer-specific diffusion weighted imaging in human primary visual cortex in vitro

    Cortex

    (2013)
  • C. Lu et al.

    Surface evolution under curvature flows

    J. Vis. Commun. Image Represent.

    (2002)
  • J.A. McNab et al.

    Surface based analysis of diffusion orientation for identifying architectonic domains in the in vivo human cortex

    Neuroimage

    (2013)
  • R.E. Smith et al.

    Anatomically-constrained tractography: improved diffusion mri streamlines tractography through effective use of anatomical information

    Neuroimage

    (2012)
  • S.N. Sotiropoulos et al.

    Advances in diffusion mri acquisition and processing in the human connectome project

    Neuroimage

    (2013)
  • J.D. Tournier et al.

    Robust determination of the fibre orientation distribution in diffusion mri: non-negativity constrained super-resolved spherical deconvolution

    NeuroImage

    (2007)
  • D.C. Van Essen et al.

    The wu-minn human connectome project: an overview

    Neuroimage

    (2013)
  • M. Waehnert et al.

    Anatomically motivated modeling of cortical laminae

    Neuroimage

    (2014)
  • S. Wakana et al.

    Reproducibility of quantitative tractography methods applied to cerebral white matter

    Neuroimage

    (2007)
  • G. Auzias et al.

    Model-driven harmonic parameterization of the cortical surface: hip-hop

    IEEE Trans. Med. Imag.

    (2013)
  • M.D. Budde et al.

    Quantification of anisotropy and fiber orientation in human brain histological sections

    Front. Integr. Neurosci.

    (2013)
  • R. Caminiti et al.

    Diameter, length, speed, and conduction delay of callosal axons in macaque monkeys and humans: comparing data from histology and magnetic resonance imaging diffusion tractography

    J. Neurosci.

    (2013)
  • K. Crane et al.

    Robust fairing via conformal curvature flow

    ACM Trans. Graph.

    (2013)
  • A. Daducci et al.

    COMMIT: Convex optimization modeling for microstructure informed tractography

    IEEE Trans Med Imaging

    (2014)
  • K. Deckelnick et al.

    Computation of geometric partial differential equations and mean curvature flow

    Acta Numer.

    (2005)
  • M. Desbrun et al.

    Implicit fairing of irregular meshes using diffusion and curvature flow

  • M. Descoteaux et al.

    Deterministic and probabilistic tractography based on complex fibre orientation distributions

    IEEE Trans. Med. Imag.

    (2009)
  • Dipy,. Diffusion Imaging in Python - Neuroimaging in Python. URL:...
  • E. Garyfallidis et al.

    Dipy, a library for the analysis of diffusion mri data

    Front. Neuroinf.

    (2014)
  • G. Girard et al.

    Structural connectivity reproducibility through multiple acquisitions

  • P. Hagmann et al.

    Mapping human whole-brain structural networks with diffusion mri

    PLos One

    (2007)
  • S. Jbabdi et al.

    Tractography: where do we go from here?

    Brain Connect.

    (2011)
  • S. Jbabdi et al.

    Measuring macroscopic brain connections in vivo

    Nat. Neurosci.

    (2015)
  • Cited by (0)

    View full text