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Mesh-based anatomically-constrained tractography for effective tracking termination and structural connectome construction

Authors:

Abstract

This study introduces a novel diffusion MRI streamlines tractography framework called mesh-based anatomically-constrained tractography (MACT) that incorporates high-resolution surface models of various brain tissues as more accurate anatomical constraints in the fibre-tracking process. By detecting intersections between streamlines and tissue surfaces, MACT can effectively provide meaningful track terminations and inter-areal connections by associating streamlines with the structural labels of the intersected surfaces. This therefore minimises uncertainties caused by heuristic mechanisms of assigning streamlines to labelled structures in common image-based approaches. Methods that investigate the tractogram-based structural connectivity should benefit from the improved connectome reconstruction using the proposed technique.
00580058
Mesh-based anatomically-constrained tractographyMesh-based anatomically-constrained tractography
for eective tracking termination and structuralfor eective tracking termination and structural
connectome constructionconnectome construction
Chun-Hung Yeh , Robert Elton Smith , Thijs Dhollander , and Alan Connelly
The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia, The Florey
Department of Neuroscience, University of Melbourne, Melbourne, Australia
SynopsisSynopsis
This study introduces a novel diusion MRI streamlines tractography frameworkThis study introduces a novel diusion MRI streamlines tractography framework
called mesh-based anatomically-constrained tractography (MACT) thatcalled mesh-based anatomically-constrained tractography (MACT) that
incorporates high-resolution surface models of various brain tissues as moreincorporates high-resolution surface models of various brain tissues as more
accurate anatomical constraints in the bre-tracking process. By detectingaccurate anatomical constraints in the bre-tracking process. By detecting
intersections between streamlines and tissue surfaces, MACT can eectivelyintersections between streamlines and tissue surfaces, MACT can eectively
provide meaningful track terminations and inter-areal connections by associatingprovide meaningful track terminations and inter-areal connections by associating
streamlines with the structural labels of the intersected surfaces. This thereforestreamlines with the structural labels of the intersected surfaces. This therefore
minimises uncertainties caused by heuristic mechanisms of assigningminimises uncertainties caused by heuristic mechanisms of assigning
streamlines to labelled structures in common image-based approaches. Methodsstreamlines to labelled structures in common image-based approaches. Methods
that investigate the tractogram-based structural connectivity should benet fromthat investigate the tractogram-based structural connectivity should benet from
the improved connectome reconstruction using the proposed technique.the improved connectome reconstruction using the proposed technique.
PurposePurpose
Anatomically-constrained tractography (ACT ) and similar variants can improve the biological
plausibility of streamlines tractograms by using anatomical information to control termination of
bre tracking. These techniques typically use the tissue partial volume maps (PVMs) of T1-based
brain segmentation as anatomical constraints to ensure that streamlines terminate within either
cortical grey matter (CGM), sub-cortical grey matter (SGM), or brainstem. Streamline endpoints
following these methods are often determined by the grey matter (GM) PVM, which can however
be inconsistent with the brain parcellation image (see Figure 1). Such spatial mismatch can
signicantly limit the number of identiable pairwise connections in the process of structural
connectome construction , and therefore additional node assignment mechanisms are required
to compensate for such discrepancy . To address this problem, this study proposes a novel
mesh-based ACT (abbreviated to MACT) technique that incorporates more accurate anatomical
constraints using high-resolution surface representations of brain tissues. Diering from the PVM-
based ACT, MACT incorporates multiple tissue surface models into the tractography process and
directly identies intersections between streamlines and surfaces; thus, streamlines are not only
terminated with meaningful endpoints but also eectively acquire structural labels dened on the
tissue surfaces. This may avoid potential ambiguities induced by assigning streamlines to brain
parcellations in the common image-based methods and should therefore be advantageous to
structural connectome construction.
MethodsMethods
Tissue surface models: An in-house MATLAB (MathWorks) script was used to generate tissue
surface models that serve as anatomical constraints in MACT. In this study, four types of brain
tissue surfaces were created from structural T1s (0.9-mm isotropic resolution) acquired on a
Siemens 3T MRI scanner with the following steps:
(a) CGM: Brainder was employed to crop the closed CGM surfaces generated by FreeSurfer in
order to allow inter-hemisphere connections as well as connections to brainstem and cerebellum.
(b) SGM: The SGM surfaces obtained from FSL's FIRST were combined together.
(c) Cerebellum (CBM): FreeSurfer's surface tessellation and smoothing functions were applied to
convert CBM labels to surfaces.
(d) Ventricles (or CSF): The surface meshes of the ventricles created from FreeSurfer's parcellation
image were merged together.
(e) All of the above-mentioned tissue surfaces were aligned to the scanner coordinate.
Surface seeding: The mechanism of homogeneously seeding on the tissue surface is developed by
taking the area of triangle (i.e. the mesh element) into account.
Fibre tracking: MACT follows the principle of ACT to accept or reject streamlines on the basis of
both anatomical and tracking criteria , where the main dierence is that MACT detects possible
intersecting tissue types at each bre-tracking step. Three-dimensional surface lookup tables are
developed to accelerate the processing speed. MACT is integrated with the features of ACT
including the back-tracking algorithm and the tailored anatomical priors for SGM.
1 1 1 1,2
1 2
1 2
3
3,4
5 6
7
1
1
FiguresFigures
(a) With ACT , streamline
endpoints (coloured in purple)
occur at the GM-WM interface or
within the subcortical GM. (b)
Due to factors such as
discretisation of structural
labels, many of these endpoints
(purple points) do not locate
inside the FreeSurfer
parcellation image (blue ribbon)
and thus are not assigned to a
label. (c) The T1 image shown in
(a-b) is replaced by the GM PVM
from FSL. (d) A zoom region of
(c) illustrates the discrepancy
(pointed by arrows) between FSL
segmentation and FreeSurfer
parcellation, revealing that
streamlines cannot be assigned
purely based on the voxels
where the endpoints reside.
The preparation of high-
resolution tissue surface models
for MACT. Top row: the open
outer CGM surfaces of both
hemispheres. Second row: the
open inner CGM surfaces of
both hemispheres. The dashed
rectangles highlight the regions
1
ResultsResults
Figure 2 shows the outcomes of tissue surface preparation. Following the mesh processing, the
inner and outer CGM surfaces became open surfaces; this enables, for instance, connections
between brain hemispheres. Figure 3 demonstrates the aligned meshes that are used as
anatomical constraints in MACT according to the tissue type. Figure 4 shows the results of MACT,
in which bre orientation distributions were computed from DWIs (2.5-mm cubic voxels, 60
directions, b=3000 s/mm ) using constrained spherical deconvolution . Five million streamlines
were generated using the iFOD2 algorithm with the surface seeding method. All the streamline
endpoints can successfully identify structural label information dened on the surfaces.
DiscussionDiscussion
Our data demonstrates that MACT is an eective technique for generating streamlines
tractograms with meaningful track terminations determined by high-resolution surface meshes of
brain tissues. The MACT framework enables using the same source anatomical information
dened by various tissue surfaces at both the bre-tracking and connectome construction stages,
which can therefore minimise uncertainties induced by additional mechanisms commonly used in
the image-based approaches for assigning streamlines to structural labels . Also note that in the
processing framework of the Human Connectome Project , the tractogram map or direct cortical
connectivity is obtained by only using the CGM surface to try to ensure that each streamline
possesses at least two intersections. MACT has the advantage of incorporating more
comprehensive surface models as anatomical constraints into the tractography process as well as
compatibility with the emerging structure-informed tractogram ltering method for quantitative
tractogram reconstruction . Altogether, the proposed method should be advantageous to
structural connectome construction.
ConclusionConclusion
We introduce a novel tractography framework that incorporates high-resolution surface models of
brain tissues into the tractography process. Investigations of tractogram-based structural
connectivity should benet from the proposed method, which can also be combined with other
neuroimaging modalities where the data is measured natively on the brain surface .
AcknowledgementsAcknowledgements
ReferencesReferences
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2 8
9
3
10
11-13
14
(e.g. corpus callosum) cropped
by the mesh processing step
(see Methods). Third row: the
combined surface of the SGM
structures, including nucleus
accumbens, amygdala, caudate
nucleus, hippocampus,
pallidum, putamen, and
thalamus for both hemispheres.
Bottom row: the integrated
surface of the ventricular system
(i.e. the lateral, third, fourth, and
fth ventricles) and cerebellum
surfaces converted from
FreeSurfer's parcellation image.
The tissue surface models are
transformed and aligned into
the scanner space, and then
serve as anatomical constraints
in MACT (grey: outer CGM and
outer CBM; red: inner CGM;
yellow: inner CBM; green: SGM;
blue: ventricles/CSF). Note that
for clarity, this gure only shows
tissue structures in the left
hemisphere. Bottom right: The
combination of inner CGM, SGM,
and inner CBM surfaces is
analogous to the GM-WM
interface, from which
streamlines can be initiated by
the surface seeding mechanism
developed in the present study.
Surfaces are displayed using
3DSlicer (http://www.slicer.org).
13. Yeh CH, et al. Correction for diusion MRI bre tracking biases: The consequences for
structural connectomic metrics. NeuroImage. 2016; 142: 150-62.
14. Glasser MF, et al. The Human Connectome Project's neuroimaging approach. Nat Neurosci.
2016; 19(9): 1175-87.
The streamlines tractogram and
the distribution of streamline
endpoints following MACT. (a-c)
Streamlines and endpoints
coloured in blue and yellow
respectively. (d) The same slice
as (c) with streamlines coloured-
coded according to their
orientations (red: left-right;
green: anterior-posterior; blue:
inferior-superior). All endpoints
can be successfully associated
with brain structural labels
dened on the surface for
connectome construction.
Proc. Intl. Soc. Mag. Reson. Med. 25 (2017)Proc. Intl. Soc. Mag. Reson. Med. 25 (2017) 00580058
... This knowledge allows us to impose the following anatomically-relevant constraints to ensure they are consistent with the nature of WM fibers, including: a) fibers should reach at least the interface of GM and WM at both ends; b) fibers do not terminate either in the middle of WM or in CSF. This is the rationale behind the so-called anatomically-constrained tractography 35 or alternatives [39][40][41] ; anatomical priors can be obtained from tissue segmentation or surface reconstruction of high-resolution anatomical MR images (usually T 1weighted images), and can be incorporated into the fibertracking process for streamline selection (Fig. 2e). This class of methods prevents biologically unrealistic connections by discarding streamlines that do not match those a priori assumptions above, as well as constrain streamlines terminations to occur only at the interface between GM and WM, within the subcortical GM, or at the spinal column. ...
... This is compatible with what we are fundamentally trying to achieve when calculating metrics of connectivity: given a basic understanding of how neurons are arranged in the brain-an axonal fiber connects two cell bodies in the GM-the ideal case therefore is that streamlines connect one GM node with another to represent a potential neuron-neuron connection. It is worth noting that this consideration is closely aligned with the general constraints adopted in anatomicallyconstrained tracking techniques 35,[39][40][41] that are designed to ensure biologically meaningful pathways (as described in subsection Streamline Termination Bias; page 4-5). The appropriate streamline termination given by these methods facilitates the streamline-to-node assignment process and is therefore beneficial for identifying connectivity between GM ROIs. ...
... Each segmentation algorithm may operate differently, leading to variations in the output images. One way to ensure that the algorithms applied to streamline termination and brain parcellation are exactly compatible is to use a surface-based approach, 40,41 where tissue surfaces with structural or functional labels are used at both tractography and connectome construction stages. This can minimize uncertainties induced by additional mechanisms typically required in the image-based approaches for assigning streamlines to brain parcels. ...
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