1,470 research outputs found
Reducing variability in along-tract analysis with diffusion profile realignment
Diffusion weighted MRI (dMRI) provides a non invasive virtual reconstruction
of the brain's white matter structures through tractography. Analyzing dMRI
measures along the trajectory of white matter bundles can provide a more
specific investigation than considering a region of interest or tract-averaged
measurements. However, performing group analyses with this along-tract strategy
requires correspondence between points of tract pathways across subjects. This
is usually achieved by creating a new common space where the representative
streamlines from every subject are resampled to the same number of points. If
the underlying anatomy of some subjects was altered due to, e.g. disease or
developmental changes, such information might be lost by resampling to a fixed
number of points. In this work, we propose to address the issue of possible
misalignment, which might be present even after resampling, by realigning the
representative streamline of each subject in this 1D space with a new method,
coined diffusion profile realignment (DPR). Experiments on synthetic datasets
show that DPR reduces the coefficient of variation for the mean diffusivity,
fractional anisotropy and apparent fiber density when compared to the unaligned
case. Using 100 in vivo datasets from the HCP, we simulated changes in mean
diffusivity, fractional anisotropy and apparent fiber density. Pairwise
Student's t-tests between these altered subjects and the original subjects
indicate that regional changes are identified after realignment with the DPR
algorithm, while preserving differences previously detected in the unaligned
case. This new correction strategy contributes to revealing effects of interest
which might be hidden by misalignment and has the potential to improve the
specificity in longitudinal population studies beyond the traditional region of
interest based analysis and along-tract analysis workflows.Comment: v4: peer-reviewed round 2 v3 : deleted some old text from before
peer-review which was mistakenly included v2 : peer-reviewed version v1:
preprint as submitted to journal NeuroImag
Fuzzy Fibers: Uncertainty in dMRI Tractography
Fiber tracking based on diffusion weighted Magnetic Resonance Imaging (dMRI)
allows for noninvasive reconstruction of fiber bundles in the human brain. In
this chapter, we discuss sources of error and uncertainty in this technique,
and review strategies that afford a more reliable interpretation of the
results. This includes methods for computing and rendering probabilistic
tractograms, which estimate precision in the face of measurement noise and
artifacts. However, we also address aspects that have received less attention
so far, such as model selection, partial voluming, and the impact of
parameters, both in preprocessing and in fiber tracking itself. We conclude by
giving impulses for future research
Fiber-Flux Diffusion Density for White Matter Tracts Analysis: Application to Mild Anomalies Localization in Contact Sports Players
We present the concept of fiber-flux density for locally quantifying white
matter (WM) fiber bundles. By combining scalar diffusivity measures (e.g.,
fractional anisotropy) with fiber-flux measurements, we define new local
descriptors called Fiber-Flux Diffusion Density (FFDD) vectors. Applying each
descriptor throughout fiber bundles allows along-tract coupling of a specific
diffusion measure with geometrical properties, such as fiber orientation and
coherence. A key step in the proposed framework is the construction of an FFDD
dissimilarity measure for sub-voxel alignment of fiber bundles, based on the
fast marching method (FMM). The obtained aligned WM tract-profiles enable
meaningful inter-subject comparisons and group-wise statistical analysis. We
demonstrate our method using two different datasets of contact sports players.
Along-tract pairwise comparison as well as group-wise analysis, with respect to
non-player healthy controls, reveal significant and spatially-consistent FFDD
anomalies. Comparing our method with along-tract FA analysis shows improved
sensitivity to subtle structural anomalies in football players over standard FA
measurements
Using neurite orientation dispersion and density imaging and tracts constrained by underlying anatomy to differentiate between subjects along the Alzheimer's disease continuum
OBJECTIVE: To assess the involvement of the white matter of the brain in the pathology of Alzheimer’s disease. Using Neurite Orientation Density and Dispersion Imaging (NODDI) and the probabilistic white matter parcellation tool Tracula as a means for understanding whether alterations in the white matter underlie changes in perceived cognitive abilities across the spectrum from health aging to Alzheimer’s disease.
METHOD: Data were obtained from 28 participants in the Health Outreach Program for the Elderly (HOPE) at the Boston University Alzheimer’s Disease Center (BU ADC) Clinical Core Registry. MRI scans included an MPRAGE T1 scan, multi-b shell diffusion scan and a High Angular Resolution Diffusion Imaging scan (HARDI). Scans were processed with Freesurfer v6.0 and the NODDI Python2.7 toolkit. The resulting data included the orientation dispersion index (ODI) and Fractional Anisotropy (FA) values for cortical and subcortical regions in the DKT atlas space as well as specific Tracts Constrained by Underlying Anatomy (TRACULA) measurements for 18 specific established white matter tracts. Statistical models using measures of pathway integrity (FA and ODI data) were used to assess relationships with Informant Cognitive Change Index (ICCI), self-described Cognitive Change Index (CCI), and Clinical Dementia Rating (CDR) values.
RESULTS: Measures of white matter integrity within several tracts predicted ICCI and CDR well in statistical models. FA and ODI values of the bilateral superior longitudinal fasciculi, inferior longitudinal fasciculi, and the cingulum bundle tracts were all related to ICCI and CDR. None of the known tracts’ FA or ODI values were related to CCI.
CONCLUSIONS: Measures of white matter pathway integrity were predictive of ICCI and CDR scores but not CCI. These finding support the notion that self-report of cognitive abilities may be compromised by alterations in insight and reinforce the need for informed study partners and clinical ratings to evaluate potential MCI and AD
Bundle-specific Tractogram Distribution Estimation Using Higher-order Streamline Differential Equation
Tractography traces the peak directions extracted from fiber orientation
distribution (FOD) suffering from ambiguous spatial correspondences between
diffusion directions and fiber geometry, which is prone to producing erroneous
tracks while missing true positive connections. The peaks-based tractography
methods 'locally' reconstructed streamlines in 'single to single' manner, thus
lacking of global information about the trend of the whole fiber bundle. In
this work, we propose a novel tractography method based on a bundle-specific
tractogram distribution function by using a higher-order streamline
differential equation, which reconstructs the streamline bundles in 'cluster to
cluster' manner. A unified framework for any higher-order streamline
differential equation is presented to describe the fiber bundles with disjoint
streamlines defined based on the diffusion tensor vector field. At the global
level, the tractography process is simplified as the estimation of
bundle-specific tractogram distribution (BTD) coefficients by minimizing the
energy optimization model, and is used to characterize the relations between
BTD and diffusion tensor vector under the prior guidance by introducing the
tractogram bundle information to provide anatomic priors. Experiments are
performed on simulated Hough, Sine, Circle data, ISMRM 2015 Tractography
Challenge data, FiberCup data, and in vivo data from the Human Connectome
Project (HCP) data for qualitative and quantitative evaluation. The results
demonstrate that our approach can reconstruct the complex global fiber bundles
directly. BTD reduces the error deviation and accumulation at the local level
and shows better results in reconstructing long-range, twisting, and large
fanning tracts
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