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MRI-based investigations represent, to date, very powerful approaches for the study of the brain. One set of tools, provided by diffusion MRI, allows the non-invasive analysis of structural aspects of gray and white matter, by analyzing how water molecules diffuse within the brain. Although number of clinical studies employing diffusion MRI has grown in last years, some aspects still result poorly known or poorly understood by unfamiliar researchers and clinicians, due to their technical complexity. The main goal of the present work is to resume the main landmarks of diffusion MRI investigation and to show the current state as well as future perspectives of related methodologies. Abstract-MRI-based investigations represent, to date, very powerful approaches for the study of the brain. One set of tools, provided by diffusion MRI, allows the non-invasive analysis of structural aspects of gray and white matter, by analyzing how water molecules diffuse within the brain. Although number of clinical studies employing diffusion MRI has grown in last years, some aspects still result poorly known or poorly understood by unfamiliar researchers and clinicians, due to their technical complexity. The main goal of the present work is to resume the main landmarks of diffusion MRI investigation and to show the current state as well as future perspectives of related methodologies.
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© 2016. Alessandro Arrigo MD, Alessandro Calamuneri PhD & Enricomaria Mormina MD. This is a research/review paper,
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Global Journal of Medical Research: D
Radiology, Diagnostic Imaging and Instrumentation
Volume 16 Issue 1 Version 1.0 Year 2016
Type: Double Blind Peer Reviewed International Research Journal
Publisher: Global Journals Inc. (USA)
Online ISSN: 2249-4618 & Print ISSN: 0975-5888
Diffusion MRI of Human Brain: Key Points and Innovations
By Alessandro Arrigo MD, Alessandro Calamuneri PhD & Enricomaria Mormina MD
University of Messina
Abstract-
MRI-based investigations represent, to date, very powerful approaches for the study of
the brain. One set of tools, provided by diffusion MRI, allows the non-invasive analysis of
structural aspects of gray and white matter, by analyzing how water molecules diffuse within the
brain. Although number of clinical studies employing diffusion MRI has grown in last years, some
aspects still result poorly known or poorly understood by unfamiliar researchers and clinicians,
due to their technical complexity. The main goal of the present work is to resume the main
landmarks of diffusion MRI investigation and to show the current state as well as future
perspectives of related methodologies.
Keywords: magnetic resonance imaging, diffusion-weighted imaging, diffusion models, diffusion
tensor imaging, constrained spherical deconvolution, tracto-graphy.
GJMR-D Classification : NLMC Code: WL 348
DiffusionMRIofHumanBrainKeyPointsandInnovations
Strictly as per the compliance and regulations of:
Diffusion MRI of Human Brain: Key Points and
Innovations
Alessandro Arrigo MD α, Alessandro Calamuneri PhD σ & Enricomaria Mormina MD ρ
Abstract-
MRI-based investigations represent, to date, very
powerful approaches for the study of the brain. One set of
tools, provided by diffusion MRI, allows the non-invasive
analysis of structural aspects of gray and white matter, by
analyzing how water molecules diffuse within the brain.
Although number of clinical studies employing diffusion MRI
has grown in last years, some aspects still result poorly known
or poorly understood by unfamiliar researchers and clinicians,
due to their technical complexity. The main goal of the present
work is to resume the main landmarks of diffusion MRI
investigation and to show the current state as well as future
perspectives of related methodologies.
Keywords: magnetic resonance imaging, diffusion-
weighted imaging, diffusion models, diffusion tensor
imaging, constrained spherical deconvolution, tracto-
graphy.
I. Introduction
iffusion MRI (dMRI) represents nowadays a
powerful tool for the non-invasive investigation of
the brain. It allows to perform both qualitative and
quantitative evaluation of brain features as well as of its
alterations, with particular regards to white matter ones.
All diffusion-based techniques are dedicated to the
analysis of signals provided by the diffusion process of
water molecules within brain tissues. Goal of this
manuscript is two-fold: firstly, we want to provide a
summary of the state of the art for researchers unfamiliar
with dMRI models and related techniques; secondly, we
want to address some of future perspectives in the field.
II. Diffusion Models
Diffusion models consist in a set of algorithms
attempting to estimate how water molecules diffuse
within each voxel (imaging unit). The mostly known
model is diffusion tensor, which is the basis of Diffusion
Tensor Imaging (DTI) (Basser et al., 2000); however a
cohort of other models which overperform DTI have
been developed over the years, such as Q-ball imaging
(QBI) (Tuch, 2004), Diffusion Spectrum Imaging
(Wedeen et al., 2008), Constrained Spherical
Deconvolution (CSD)
(Tournier
et
al.,
2007),
multi-
Correspondence
Author
α
:
University of Messina, Department of
Biomedical Sciences and Morphological and Functional Imaging, Via
Consolare Valeria, 1 Messina, 98125, Italy.
e-mail: alessandro.arrigo@hotmail.com
Author
σ ρ
:
Department of Biomedical Sciences and Morphological
and Functional Images, University of Messina, Messina, Italy.
e-mails: alecalamuneri@gmail.com,
enricomaria.mormina@gmail.com
compartments models (see for instance Panagiotaki et
al., 2012). All above mentioned techniques return back a
geometrical object (e.g. the tensor for DTI) which
encodes diffusion process for each analyzed voxel; the
sensitivity as well as the type of information which can
be extracted from such objects vary according to the
algorithm/model used. Based on these objects,
qualitative and quantitative analyses can be performed.
a)
Qualitative analysis
One of the most explored applications of
diffusion MRI is tractography (Soares et al., 2013), i.e.
the reconstruction of the path followed by a given white
matter bundle. This can be achieved by means of both
deterministic (one direction assigned for each voxel)
and probabilistic (the most probable path obtained after
a given number of attempts) tractographic algorithms
(Soares et al., 2013; Behrens et al., 2007). Tractography
reconstruction outcomes strongly rely on the underlying
diffusion model used In this context, several issues can
affect the reliability of tractographic results, e.g. the
presence of voxels with multiple fiber directions
(Farquharson et al., 2013). DTI cannot handle multiple
fiber directions, as it can only provide a unique diffusion
direction. This is the reason why other more advanced
approaches outperform DTI based tractography, like
CSD (Tournier et al., 2008; Farquharson et al., 2013). An
exemplificative case showing how tractographic output
can be different according to the model used, namely
DTI and CSD, is shown in Figure 1, where corticospinal
tract and optic radiations were reconstructed with both
methods.
D
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Figure 1 :
Tractography of left corticospinal tract (A) and left optic radiations (B). Reconstructions of these two
eloquent white matter bundles were obtained by means of probabilistic CSD model (red) and DTI one (blue), which
were overlapped in order to show qualitative differences. On the right side, the schematic representation of
anatomical features of these bundles, as well as their end points in the cortex, are shown.
b)
Quantitative analysis
From each diffusion model a number of useful
features can be extracted in a given voxel. Those
features can be used to quantify WM and perform
investigation both in normal and pathological conditions.
It is important to clarify that nature and validity of
features extracted depend on a number of factors, e.g.
quality of scans used. Here we want however to keep
focus on what different diffusion model can offer. If
based on tensor model, quantitative analysis can
provide information regarding how much anisotropic is
the signal within a voxel, through a number of
parameters among which fractional anisotropy (FA) and
mean diffusivity (MD) are the most used (Soares et al.,
2013). Those measures have been considered indirect
measures of axonal integrity (Alexander et al., 2007;
Soares et al., 2013). Due to the ability of other models to
better reconstruct WM bundles, it was suggested to
sample tensor features on voxels reached by
tractographic reconstructions obtained by other
methods, like CSD (Mormina et al., 2014; Arrigo et al.,
2014; Mormina et al., 2015; Arrigo et al., 2015; Arrigo et
al., 2016).
FA measures level of anisotropy in the voxel: the
higher this number, the higher the probability that a
single predominant fiber direction is appearing in that
voxel. It has to be noticed however that (Jeurissen et al.,
2013), if we were to compare FA values obtained by
averaging within voxels sampled by means ofCSD-
based tractographic reconstruction with the same
average performed on the basis of DTI tractography, we
would observe a FA reduction. This happens because,
with CSD, voxels with multiple dominant fiber directions
are involved; as result, water diffusion anisotropy is
spread across different directions, and tensor model is
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Diffusion MRI of Human Brain: Key Points and Innovations
able to fit an overall anisotropy decreasing. Due to the
huge number of voxels showing this behavior in WM
(Jeurissen et al, 2013), new features were developed to
the better describe diffusion models. As an example,
based on CSD, Apparent Fiber Density (AFD) (Raffelt et
al., 2012), was developed to measure contribute of each
dominant direction. Figure 2 illustrates the situation.
Figure 2 :
Tractography of the left corticospinal tract obtained by means of DTI and CSD models. Some
exemplificative voxels showed differences regarding calculation of diffusion signals, if using tensor model or CSD, in
the following cases: monodirectional (a), multidirectional (b) and crossing fibers (c) voxels. The presence of (b) and
(c) affects tensor model, thus causing poor qualitative reconstruction. Moreover, also quantitative analysis is
affected, since only voxels with a well-represented principal direction are considered.
III.
Limitations, Validations and Future
Perspectives
Diffusion MRI results, particularly tractography,
are often criticized due to a number of limitations
potentially affecting outputs. Moving beyond the intrinsic
limitation represented by the impossibility to discriminate
directionality of afferent or efferent signal transmission
(Parker et al. 2013; Chung et al. 2011), as previously
described, tractographic output strongly depends on the
algorithm used for diffusion signal modelling
(Farquharson et al., 2013).Several inaccuracies caused
by possible artefactual effects as well as false positive
tracts should be also taken into account (Jones and
Cercignani, 2010). Furthermore, since tractography
represents the reconstruction of white matter paths
provided by a mathematical computation (deterministic
or probabilistic), it is often criticized by declaring that
dissection is preferable due to its ability to definitely
assess the real existence of a given connection.
However, a number of studies have validated DTI
tractographic output through histological investigations
(Seehaus et al., 2013; Gao et al., 2013; Seehaus et al.,
2015). The adoption of more advanced algorithms have
allowed a better detection of white matter bundles;
those techniques have obtained histological validations
as well (Dirby et al., 2007; Azadbakht et al., 2015).
Recently, in vivo neurite orientation dispersion and
density imaging (NODDI) (Zhang et al., 2012) was
proposed: this technique allows a multi-compartmental
analysis of the brain, i.e. separately considering glial,
axonal and extracellular components, thus restituting a
detailed profile of brain microstructure. Although
technical requirement are not easily reachable, this
represents a promising investigative technique for a
deeper study of the brain both in healthy and
pathological conditions.
Interesting future perspectives will be to make
more feasible these innovative approaches for clinical
settings as well as to integrate them with other
investigative techniques, such as electrophysiology and
transcranial magnetic stimulation.
IV. Conclusion
In this paper the main key points of diffusion
MRI investigations have been neatly described. We
wanted to provide a brief and simplified description of
the complex methodological aspects, in order to offer
necessary pills for better understanding diffusion-based
studies.
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Diffusion tensor imaging (DTI) tractography provides noninvasive measures of structural cortico-cortical connectivity of the brain. However, the agreement between DTI-tractography-based measures and histological 'ground truth' has not been quantified. In this study, we reconstructed the 3D density distribution maps (DDM) of fibers labeled with an anatomical tracer, biotinylated dextran amine (BDA), as well as DTI tractography-derived streamlines connecting the primary motor (M1) cortex to other cortical regions in the squirrel monkey brain. We evaluated the agreement in M1-cortical connectivity between the fibers labeled in the brain tissue and DTI streamlines on a regional and voxel-by-voxel basis. We found that DTI tractography is capable of providing inter-regional connectivity comparable to the neuroanatomical connectivity, but is less reliable measuring voxel-to-voxel variations within regions.
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Diffusion Tensor Imaging (DTI) studies are increasingly popular among clinicians and researchers as they provide unique insights into brain network connectivity. However, in order to optimize the use of DTI, several technical and methodological aspects must be factored in. These include decisions on: acquisition protocol, artifact handling, data quality control, reconstruction algorithm, and visualization approaches, and quantitative analysis methodology. Furthermore, the researcher and/or clinician also needs to take into account and decide on the most suited software tool(s) for each stage of the DTI analysis pipeline. Herein, we provide a straightforward hitchhiker's guide, covering all of the workflow's major stages. Ultimately, this guide will help newcomers navigate the most critical roadblocks in the analysis and further encourage the use of DTI.
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Object: Diffusion-based MRI tractography is an imaging tool increasingly used in neurosurgical procedures to generate 3D maps of white matter pathways as an aid to identifying safe margins of resection. The majority of white matter fiber tractography software packages currently available to clinicians rely on a fundamentally flawed framework to generate fiber orientations from diffusion-weighted data, namely diffusion tensor imaging (DTI). This work provides the first extensive and systematic exploration of the practical limitations of DTI-based tractography and investigates whether the higher-order tractography model constrained spherical deconvolution provides a reasonable solution to these problems within a clinically feasible timeframe. Methods: Comparison of tractography methodologies in visualizing the corticospinal tracts was made using the diffusion-weighted data sets from 45 healthy controls and 10 patients undergoing presurgical imaging assessment. Tensor-based and constrained spherical deconvolution-based tractography methodologies were applied to both patients and controls. Results: Diffusion tensor imaging-based tractography methods (using both deterministic and probabilistic tractography algorithms) substantially underestimated the extent of tracks connecting to the sensorimotor cortex in all participants in the control group. In contrast, the constrained spherical deconvolution tractography method consistently produced the biologically expected fan-shaped configuration of tracks. In the clinical cases, in which tractography was performed to visualize the corticospinal pathways in patients with concomitant risk of neurological deficit following neurosurgical resection, the constrained spherical deconvolution-based and tensor-based tractography methodologies indicated very different apparent safe margins of resection; the constrained spherical deconvolution-based method identified corticospinal tracts extending to the entire sensorimotor cortex, while the tensor-based method only identified a narrow subset of tracts extending medially to the vertex. Conclusions: This comprehensive study shows that the most widely used clinical tractography method (diffusion tensor imaging-based tractography) results in systematically unreliable and clinically misleading information. The higher-order tractography model, using the same diffusion-weighted data, clearly demonstrates fiber tracts more accurately, providing improved estimates of safety margins that may be useful in neurosurgical procedures. We therefore need to move beyond the diffusion tensor framework if we are to begin to provide neurosurgeons with biologically reliable tractography information.