Dmitri Shastin1,2, Maxime Chamberland1, Greg Parker1, Chantal M. W. Tax1, Kristin Koller1, Khalid Hamandi1,2, William Gray2,3, and Derek Jones1
1School of Psychology, Cardiff University Brain Research Imaging Centre, Cardiff, United Kingdom, 2School of Medicine, Cardiff University Brain Research Imaging Centre, Cardiff, United Kingdom, 3BRAIN Biomedical Research Unit, Cardiff, United Kingdom
Synopsis
Anatomically constrained tractography aims to reduce the
number of false positive streamlines by applying anatomically realistic priors.
Grey matter-white matter interface is currently extracted based on T1
information requiring the availability of a T1 volume and its accurate co-registration
with diffusion MRI data. Here we describe an alternative method based on
multi-shell multi-tissue constrained spherical deconvolution that produces the
interface without the need for T1. We go ahead and compare our method with the
existing one and show a potential application.
Introduction
The ability of diffusion MRI (dMRI)-derived tractography to
describe white matter pathways non-invasively is of enormous scientific and
clinical utility. However, the mapping from diffusion signal to fibre orientations
is an ill-posed problem, resulting in a large number of false positive
streamlines.1
The concept of anatomically constrained tractography (ACT) aims to overcome
this limitation by applying anatomically realistic priors.2,3 One step of ACT involves defining a grey matter-white matter interface (GMWMI) based
on T1 information with a need to accurately co-register T1 and dMRI volumes. We
present a method to produce GMWMI directly from dMRI data based on multi-shell
multi-tissue constrained spherical deconvolution (MSMT-CSD)-derived fibre
orientation distributions, thereby avoiding the need for such co-registration.Methods
Acquisition and processing: One volunteer was scanned
on a 3T Siemens Connectom system. An anatomical T1 volume (voxel size: 1x1x1 mm3,
TR/TE 2300/2.81 ms) and a dMRI dataset (single-shot spin echo, echo planar, voxel
size: 2×2×2 mm3; b=0/200/500/1200/2400/4000/6000 s/mm2 in
13/20/20/30/61/61/61 directions, respectively; TR/TE 3000/59 ms) were procured.
dMRI dataset was upsampled to 1x1x1 mm3, denoised,4
corrected for signal drift5
and slicewise6
intensity outliers, Eddy current distortion and motion artefact,7 EPI distortion,8 gradient non-linearity9
and Gibbs artefact.10
Pseudo-anatomical volume generation: Fibre
orientation distribution (FOD) volumes11 were derived using 3-tissue
response function estimation12 and subsequent MSMT-CSD.13
Bias field correction14 was applied. A
pseudo-anatomical volume (PAV) was calculated by adding the intensity-normalised
white matter (WM) and grey matter (GM) FODs with the latter multiplied by 2.5
times its mean positive intensity. The product was thresholded at 7.5% of maximum
intensity to ensure a null non-brain voxel intensity (Fig.1). T1 volume was co-registered15 with PAV for comparison.
dGMWMI extraction: An exemplary WM voxel (v0)
was identified by sampling non-null voxels of thresholded WM FOD (n=30) and
finding one whose intensity was closest to the median sampled intensity. Two
methods of extracting the initial WM mask were then compared. The first (M1)
consisted of using K-means algorithm16 to split PAV voxels into two
classes and associating the class containing the v0 with WM. The
second (M2, referred to as “thresholding” analysis) consisted of
repeatedly recruiting voxels while increasing intensity threshold starting from
v0 so that more voxels were recruited with each iteration. Iteration
number was plotted against the number of recruited voxels and a seventh-degree
polynomial was fitted to the data. As “speeding up” of recruitment was expected
after most of WM was included, the first inflection point where the function
became concave upwards was determined (Fig.2). Following this step, deep grey
matter (DGM) structures and the brainstem were segmented using FSL FIRST.17 The ventricles were
segmented by applying the eroded brain mask on the cerebrospinal fluid FOD and leaving
only the component with the largest number of ‘connected’ voxels. The final dGMWMI
mask was produced by generating a boundary around the initial WM mask, applying
dilated inverted binary DGM, brainstem and ventricular masks, and drawing
additional boundaries around each DGM structure.Results
PAV extraction: The FOD-derived PAV demonstrated a
sharp grey-white matter contrast despite being derived from a 2x2x2mm3
dMRI volume (Fig. 1). This allowed for a sufficiently precise GMWMI extraction
in subsequent steps.
Initial WM mask: Fig. 2 shows how M2 identifies
the point where all WM voxels have been recruited. This translates into the
initial WM mask shown in Fig. 3 (right). In comparison with M1 (Fig.
3, left), it tends to be more constrained which better avoids the DGM
structures but can under-recruit subcortical WM.
Qualitative comparison with mrtrix 5tt2gmwmi: Fig. 4
shows the final dGMWMI, its relationship with co-registered T1, and how it
compares with T1-derived GMWMI produced in mrtrix, with good correspondence
between the cases. dGMWMI appears “cleaner” as it does not consider partial
volume effects (which may or may not be desired based on the application). Note
that the boundary can be pushed deeper into the GM if created on the outside of
the WM.Discussion
The demonstrated method allows for delineation of GMWMI with
high quality without the need for a T1 volume. This avoids the potential imperfections
resulting from T1-to-dMRI co-registration which can be particularly problematic
with high gradient strengths and especially around the cortical surfaces. An
example of such use is extraction of U-shaped fibres for studying
cortico-cortical connectivity (Fig. 5) where it can be combined with other
methods.18
A modified version of “thresholding” analysis using voxel connectivity can be applied
to improve segmentation of individual structures based on their intensities (used
to remove the cerebellum in this example). Other data types (such as fractional
anisotropy) can be similarly exploited to inform segmentation. There is a
potential advantage in computational time when FODs are already available in
sufficiently high resolution omitting the T1 to dMRI co-registration. While
this method is based on MSMT-CSD, our future direction is to test it with lower
gradients and single-shell 3-tissue response function estimation.12
Conclusions
This work demonstrates an alternative option for grey
matter-white matter interface extraction derived directly from dMRI data. It
avoids the need for T1 co-registration where it is not required for subsequent
processing steps. This allows retrospective studies with missing or corrupted
T1 data to benefit from advanced tractography methods using anatomical priors.Acknowledgements
DS
is supported by the Wellcome Trust-funded GW4 Clinical Academic Training
fellowship and Welsh Clinical Academic Track fellowship. SB is supported by the
Wellcome Trust Inspire Vacation Studenship.
CMWT is supported by a Rubicon grant (680-50-1527) from the
Netherlands Organisation for Scientific Research (NWO) and a Sir Henry Wellcome
Fellowship (215944/Z/19/Z).
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