907 research outputs found
Increasing the Analytical Accessibility of Multishell and Diffusion Spectrum Imaging Data Using Generalized Q-Sampling Conversion
Many diffusion MRI researchers, including the Human Connectome Project (HCP),
acquire data using multishell (e.g., WU-Minn consortium) and diffusion spectrum
imaging (DSI) schemes (e.g., USC-Harvard consortium). However, these data sets
are not readily accessible to high angular resolution diffusion imaging (HARDI)
analysis methods that are popular in connectomics analysis. Here we introduce a
scheme conversion approach that transforms multishell and DSI data into their
corresponding HARDI representations, thereby empowering HARDI-based analytical
methods to make use of data acquired using non-HARDI approaches. This method
was evaluated on both phantom and in-vivo human data sets by acquiring
multishell, DSI, and HARDI data simultaneously, and comparing the converted
HARDI, from non-HARDI methods, with the original HARDI data. Analysis on the
phantom shows that the converted HARDI from DSI and multishell data strongly
predicts the original HARDI (correlation coefficient > 0.9). Our in-vivo study
shows that the converted HARDI can be reconstructed by constrained spherical
deconvolution, and the fiber orientation distributions are consistent with
those from the original HARDI. We further illustrate that our scheme conversion
method can be applied to HCP data, and the converted HARDI do not appear to
sacrifice angular resolution. Thus this novel approach can benefit all
HARDI-based analysis approaches, allowing greater analytical accessibility to
non-HARDI data, including data from the HCP
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
Bayesian uncertainty quantification in linear models for diffusion MRI
Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue
microstructure. By fitting a model to the dMRI signal it is possible to derive
various quantitative features. Several of the most popular dMRI signal models
are expansions in an appropriately chosen basis, where the coefficients are
determined using some variation of least-squares. However, such approaches lack
any notion of uncertainty, which could be valuable in e.g. group analyses. In
this work, we use a probabilistic interpretation of linear least-squares
methods to recast popular dMRI models as Bayesian ones. This makes it possible
to quantify the uncertainty of any derived quantity. In particular, for
quantities that are affine functions of the coefficients, the posterior
distribution can be expressed in closed-form. We simulated measurements from
single- and double-tensor models where the correct values of several quantities
are known, to validate that the theoretically derived quantiles agree with
those observed empirically. We included results from residual bootstrap for
comparison and found good agreement. The validation employed several different
models: Diffusion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI)
and Constrained Spherical Deconvolution (CSD). We also used in vivo data to
visualize maps of quantitative features and corresponding uncertainties, and to
show how our approach can be used in a group analysis to downweight subjects
with high uncertainty. In summary, we convert successful linear models for dMRI
signal estimation to probabilistic models, capable of accurate uncertainty
quantification.Comment: Added results from a group analysis and a comparison with residual
bootstra
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
Ball and rackets: inferring fiber fanning from diffusion-weighted MRI
A number of methods have been proposed for resolving crossing fibers from diffusion-weighted (DW) MRI. However, other complex fiber geometries have drawn minimal attention. In this study, we focus on fiber orientation dispersion induced by within-voxel fanning. We use a multi-compartment, model-based approach to estimate fiber dispersion. Bingham distributions are employed to represent continuous distributions of fiber orientations, centered around a main orientation, and capturing anisotropic dispersion. We evaluate the accuracy of the model for different simulated fanning geometries, under different acquisition protocols and we illustrate the high SNR and angular resolution needs. We also perform a qualitative comparison between our parametric approach and five popular non-parametric techniques that are based on orientation distribution functions (ODFs). This comparison illustrates how the same underlying geometry can be depicted by different methods. We apply the proposed model on high-quality, post-mortem macaque data and present whole-brain maps of fiber dispersion, as well as exquisite details on the local anatomy of fiber distributions in various white matter regions
Evaluating the accuracy of diffusion MRI models in white matter
Models of diffusion MRI within a voxel are useful for making inferences about
the properties of the tissue and inferring fiber orientation distribution used
by tractography algorithms. A useful model must fit the data accurately.
However, evaluations of model-accuracy of some of the models that are commonly
used in analyzing human white matter have not been published before. Here, we
evaluate model-accuracy of the two main classes of diffusion MRI models. The
diffusion tensor model (DTM) summarizes diffusion as a 3-dimensional Gaussian
distribution. Sparse fascicle models (SFM) summarize the signal as a linear sum
of signals originating from a collection of fascicles oriented in different
directions. We use cross-validation to assess model-accuracy at different
gradient amplitudes (b-values) throughout the white matter. Specifically, we
fit each model to all the white matter voxels in one data set and then use the
model to predict a second, independent data set. This is the first evaluation
of model-accuracy of these models. In most of the white matter the DTM predicts
the data more accurately than test-retest reliability; SFM model-accuracy is
higher than test-retest reliability and also higher than the DTM, particularly
for measurements with (a) a b-value above 1000 in locations containing fiber
crossings, and (b) in the regions of the brain surrounding the optic
radiations. The SFM also has better parameter-validity: it more accurately
estimates the fiber orientation distribution function (fODF) in each voxel,
which is useful for fiber tracking
Doctor of Philosophy
dissertationDiffusion magnetic resonance imaging (dMRI) has become a popular technique to detect brain white matter structure. However, imaging noise, imaging artifacts, and modeling techniques, etc., create many uncertainties, which may generate misleading information for further analysis or applications, such as surgical planning. Therefore, how to analyze, effectively visualize, and reduce these uncertainties become very important research questions. In this dissertation, we present both rank-k decomposition and direct decomposition approaches based on spherical deconvolution to decompose the fiber directions more accurately for high angular resolution diffusion imaging (HARDI) data, which will reduce the uncertainties of the fiber directions. By applying volume rendering techniques to an ensemble of 3D orientation distribution function (ODF) glyphs, which we call SIP functions of diffusion shapes, one can elucidate the complex heteroscedastic structural variation in these local diffusion shapes. Furthermore, we quantify the extent of this variation by measuring the fraction of the volume of these shapes, which is consistent across all noise levels, the certain volume ratio. To better understand the uncertainties in white matter fiber tracks, we propose three metrics to quantify the differences between the results of diffusion tensor magnetic resonance imaging (DT-MRI) fiber tracking algorithms: the area between corresponding fibers of each bundle, the Earth Mover's Distance (EMD) between two fiber bundle volumes, and the current distance between two fiber bundle volumes. Based on these metrics, we discuss an interactive fiber track comparison visualization toolkit we have developed to visualize these uncertainties more efficiently. Physical phantoms, with high repeatability and reproducibility, are also designed with the hope of validating the dMRI techniques. In summary, this dissertation provides a better understanding about uncertainties in diffusion magnetic resonance imaging: where and how much are the uncertainties? How do we reduce these uncertainties? How can we possibly validate our algorithms
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