4,088 research outputs found
Fiber Orientation Estimation Guided by a Deep Network
Diffusion magnetic resonance imaging (dMRI) is currently the only tool for
noninvasively imaging the brain's white matter tracts. The fiber orientation
(FO) is a key feature computed from dMRI for fiber tract reconstruction.
Because the number of FOs in a voxel is usually small, dictionary-based sparse
reconstruction has been used to estimate FOs with a relatively small number of
diffusion gradients. However, accurate FO estimation in regions with complex FO
configurations in the presence of noise can still be challenging. In this work
we explore the use of a deep network for FO estimation in a dictionary-based
framework and propose an algorithm named Fiber Orientation Reconstruction
guided by a Deep Network (FORDN). FORDN consists of two steps. First, we use a
smaller dictionary encoding coarse basis FOs to represent the diffusion
signals. To estimate the mixture fractions of the dictionary atoms (and thus
coarse FOs), a deep network is designed specifically for solving the sparse
reconstruction problem. Here, the smaller dictionary is used to reduce the
computational cost of training. Second, the coarse FOs inform the final FO
estimation, where a larger dictionary encoding dense basis FOs is used and a
weighted l1-norm regularized least squares problem is solved to encourage FOs
that are consistent with the network output. FORDN was evaluated and compared
with state-of-the-art algorithms that estimate FOs using sparse reconstruction
on simulated and real dMRI data, and the results demonstrate the benefit of
using a deep network for FO estimation.Comment: A shorter version is accepted by MICCAI 201
Investigating microstructural variation in the human hippocampus using non-negative matrix factorization
In this work we use non-negative matrix factorization to identify patterns of microstructural variance in the human hippocampus. We utilize high-resolution structural and diffusion magnetic resonance imaging data from the Human Connectome Project to query hippocampus microstructure on a multivariate, voxelwise basis. Application of non-negative matrix factorization identifies spatial components (clusters of voxels sharing similar covariance patterns), as well as subject weightings (individual variance across hippocampus microstructure). By assessing the stability of spatial components as well as the accuracy of factorization, we identified 4 distinct microstructural components. Furthermore, we quantified the benefit of using multiple microstructural metrics by demonstrating that using three microstructural metrics (T1-weighted/T2-weighted signal, mean diffusivity and fractional anisotropy) produced more stable spatial components than when assessing metrics individually. Finally, we related individual subject weightings to demographic and behavioural measures using a partial least squares analysis. Through this approach we identified interpretable relationships between hippocampus microstructure and demographic and behavioural measures. Taken together, our work suggests non-negative matrix factorization as a spatially specific analytical approach for neuroimaging studies and advocates for the use of multiple metrics for data-driven component analyses
Hippocampal subfields and limbic white matter jointly predict learning rate in older adults
First published online: 04 December 2019Age-related memory impairments have been linked to differences in structural brain parameters, including cerebral white matter (WM) microstructure and hippocampal (HC) volume, but their combined influences are rarely investigated. In a population-based sample of 337 older participants aged 61-82 years (Mage = 69.66, SDage = 3.92 years), we modeled the independent and joint effects of limbic WM microstructure and HC subfield volumes on verbal learning. Participants completed a verbal learning task of recall over five repeated trials and underwent magnetic resonance imaging (MRI), including structural and diffusion scans. We segmented three HC subregions on high-resolution MRI data and sampled mean fractional anisotropy (FA) from bilateral limbic WM tracts identified via deterministic fiber tractography. Using structural equation modeling, we evaluated the associations between learning rate and latent factors representing FA sampled from limbic WM tracts, and HC subfield volumes, and their latent interaction. Results showed limbic WM and the interaction of HC and WM-but not HC volume alone-predicted verbal learning rates. Model decomposition revealed HC volume is only positively associated with learning rate in individuals with higher WM anisotropy. We conclude that the structural characteristics of limbic WM regions and HC volume jointly contribute to verbal learning in older adults
Spherical deconvolution of multichannel diffusion MRI data with non-Gaussian noise models and spatial regularization
Spherical deconvolution (SD) methods are widely used to estimate the
intra-voxel white-matter fiber orientations from diffusion MRI data. However,
while some of these methods assume a zero-mean Gaussian distribution for the
underlying noise, its real distribution is known to be non-Gaussian and to
depend on the methodology used to combine multichannel signals. Indeed, the two
prevailing methods for multichannel signal combination lead to Rician and
noncentral Chi noise distributions. Here we develop a Robust and Unbiased
Model-BAsed Spherical Deconvolution (RUMBA-SD) technique, intended to deal with
realistic MRI noise, based on a Richardson-Lucy (RL) algorithm adapted to
Rician and noncentral Chi likelihood models. To quantify the benefits of using
proper noise models, RUMBA-SD was compared with dRL-SD, a well-established
method based on the RL algorithm for Gaussian noise. Another aim of the study
was to quantify the impact of including a total variation (TV) spatial
regularization term in the estimation framework. To do this, we developed TV
spatially-regularized versions of both RUMBA-SD and dRL-SD algorithms. The
evaluation was performed by comparing various quality metrics on 132
three-dimensional synthetic phantoms involving different inter-fiber angles and
volume fractions, which were contaminated with noise mimicking patterns
generated by data processing in multichannel scanners. The results demonstrate
that the inclusion of proper likelihood models leads to an increased ability to
resolve fiber crossings with smaller inter-fiber angles and to better detect
non-dominant fibers. The inclusion of TV regularization dramatically improved
the resolution power of both techniques. The above findings were also verified
in brain data
Disconnected aging: cerebral white matter integrity and age-related differences in cognition.
Cognition arises as a result of coordinated processing among distributed brain regions and disruptions to communication within these neural networks can result in cognitive dysfunction. Cortical disconnection may thus contribute to the declines in some aspects of cognitive functioning observed in healthy aging. Diffusion tensor imaging (DTI) is ideally suited for the study of cortical disconnection as it provides indices of structural integrity within interconnected neural networks. The current review summarizes results of previous DTI aging research with the aim of identifying consistent patterns of age-related differences in white matter integrity, and of relationships between measures of white matter integrity and behavioral performance as a function of adult age. We outline a number of future directions that will broaden our current understanding of these brain-behavior relationships in aging. Specifically, future research should aim to (1) investigate multiple models of age-brain-behavior relationships; (2) determine the tract-specificity versus global effect of aging on white matter integrity; (3) assess the relative contribution of normal variation in white matter integrity versus white matter lesions to age-related differences in cognition; (4) improve the definition of specific aspects of cognitive functioning related to age-related differences in white matter integrity using information processing tasks; and (5) combine multiple imaging modalities (e.g., resting-state and task-related functional magnetic resonance imaging; fMRI) with DTI to clarify the role of cerebral white matter integrity in cognitive aging
Estimation of Fiber Orientations Using Neighborhood Information
Data from diffusion magnetic resonance imaging (dMRI) can be used to
reconstruct fiber tracts, for example, in muscle and white matter. Estimation
of fiber orientations (FOs) is a crucial step in the reconstruction process and
these estimates can be corrupted by noise. In this paper, a new method called
Fiber Orientation Reconstruction using Neighborhood Information (FORNI) is
described and shown to reduce the effects of noise and improve FO estimation
performance by incorporating spatial consistency. FORNI uses a fixed tensor
basis to model the diffusion weighted signals, which has the advantage of
providing an explicit relationship between the basis vectors and the FOs. FO
spatial coherence is encouraged using weighted l1-norm regularization terms,
which contain the interaction of directional information between neighbor
voxels. Data fidelity is encouraged using a squared error between the observed
and reconstructed diffusion weighted signals. After appropriate weighting of
these competing objectives, the resulting objective function is minimized using
a block coordinate descent algorithm, and a straightforward parallelization
strategy is used to speed up processing. Experiments were performed on a
digital crossing phantom, ex vivo tongue dMRI data, and in vivo brain dMRI data
for both qualitative and quantitative evaluation. The results demonstrate that
FORNI improves the quality of FO estimation over other state of the art
algorithms.Comment: Journal paper accepted in Medical Image Analysis. 35 pages and 16
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