962 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
Connectivity-enhanced diffusion analysis reveals white matter density disruptions in first episode and chronic schizophrenia.
Reduced fractional anisotropy (FA) is a well-established correlate of schizophrenia, but it remains unclear whether these tensor-based differences are the result of axon damage and/or organizational changes and whether the changes are progressive in the adult course of illness. Diffusion MRI data were collected in 81 schizophrenia patients (54 first episode and 27 chronic) and 64 controls. Analysis of FA was combined with "fixel-based" analysis, the latter of which leverages connectivity and crossing-fiber information to assess both fiber bundle density and organizational complexity (i.e., presence and magnitude of off-axis diffusion signal). Compared with controls, patients with schizophrenia displayed clusters of significantly lower FA in the bilateral frontal lobes, right dorsal centrum semiovale, and the left anterior limb of the internal capsule. All FA-based group differences overlapped substantially with regions containing complex fiber architecture. FA within these clusters was positively correlated with principal axis fiber density, but inversely correlated with both secondary/tertiary axis fiber density and voxel-wise fiber complexity. Crossing fiber complexity had the strongest (inverse) association with FA (r = -0.82). When crossing fiber structure was modeled in the MRtrix fixel-based analysis pipeline, patients exhibited significantly lower fiber density compared to controls in the dorsal and posterior corpus callosum (central, postcentral, and forceps major). Findings of lower FA in patients with schizophrenia likely reflect two inversely related signals: reduced density of principal axis fiber tracts and increased off-axis diffusion sources. Whereas the former confirms at least some regions where myelin and or/axon count are lower in schizophrenia, the latter indicates that the FA signal from principal axis fiber coherence is broadly contaminated by macrostructural complexity, and therefore does not necessarily reflect microstructural group differences. These results underline the need to move beyond tensor-based models in favor of acquisition and analysis techniques that can help disambiguate different sources of white matter disruptions associated with schizophrenia
Single- and Multiple-Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes
In diffusion MRI (dMRI), a good sampling scheme is important for efficient
acquisition and robust reconstruction. Diffusion weighted signal is normally
acquired on single or multiple shells in q-space. Signal samples are typically
distributed uniformly on different shells to make them invariant to the
orientation of structures within tissue, or the laboratory coordinate frame.
The Electrostatic Energy Minimization (EEM) method, originally proposed for
single shell sampling scheme in dMRI, was recently generalized to multi-shell
schemes, called Generalized EEM (GEEM). GEEM has been successfully used in the
Human Connectome Project (HCP). However, EEM does not directly address the goal
of optimal sampling, i.e., achieving large angular separation between sampling
points. In this paper, we propose a more natural formulation, called Spherical
Code (SC), to directly maximize the minimal angle between different samples in
single or multiple shells. We consider not only continuous problems to design
single or multiple shell sampling schemes, but also discrete problems to
uniformly extract sub-sampled schemes from an existing single or multiple shell
scheme, and to order samples in an existing scheme. We propose five algorithms
to solve the above problems, including an incremental SC (ISC), a sophisticated
greedy algorithm called Iterative Maximum Overlap Construction (IMOC), an 1-Opt
greedy method, a Mixed Integer Linear Programming (MILP) method, and a
Constrained Non-Linear Optimization (CNLO) method. To our knowledge, this is
the first work to use the SC formulation for single or multiple shell sampling
schemes in dMRI. Experimental results indicate that SC methods obtain larger
angular separation and better rotational invariance than the state-of-the-art
EEM and GEEM. The related codes and a tutorial have been released in DMRITool.Comment: Accepted by IEEE transactions on Medical Imaging. Codes have been
released in dmritool
https://diffusionmritool.github.io/tutorial_qspacesampling.htm
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
A Unified Single-stage Learning Model for Estimating Fiber Orientation Distribution Functions on Heterogeneous Multi-shell Diffusion-weighted MRI
Diffusion-weighted (DW) MRI measures the direction and scale of the local
diffusion process in every voxel through its spectrum in q-space, typically
acquired in one or more shells. Recent developments in micro-structure imaging
and multi-tissue decomposition have sparked renewed attention to the radial
b-value dependence of the signal. Applications in tissue classification and
micro-architecture estimation, therefore, require a signal representation that
extends over the radial as well as angular domain. Multiple approaches have
been proposed that can model the non-linear relationship between the DW-MRI
signal and biological microstructure. In the past few years, many deep
learning-based methods have been developed towards faster inference speed and
higher inter-scan consistency compared with traditional model-based methods
(e.g., multi-shell multi-tissue constrained spherical deconvolution). However,
a multi-stage learning strategy is typically required since the learning
process relied on various middle representations, such as simple harmonic
oscillator reconstruction (SHORE) representation. In this work, we present a
unified dynamic network with a single-stage spherical convolutional neural
network, which allows efficient fiber orientation distribution function (fODF)
estimation through heterogeneous multi-shell diffusion MRI sequences. We study
the Human Connectome Project (HCP) young adults with test-retest scans. From
the experimental results, the proposed single-stage method outperforms prior
multi-stage approaches in repeated fODF estimation with shell dropoff and
single-shell DW-MRI sequences
-Equivariant Networks for Spherical Deconvolution in Diffusion MRI
We present Roto-Translation Equivariant Spherical Deconvolution (RT-ESD), an
equivariant framework for sparse deconvolution of volumes
where each voxel contains a spherical signal. Such 6D data naturally arises in
diffusion MRI (dMRI), a medical imaging modality widely used to measure
microstructure and structural connectivity. As each dMRI voxel is typically a
mixture of various overlapping structures, there is a need for blind
deconvolution to recover crossing anatomical structures such as white matter
tracts. Existing dMRI work takes either an iterative or deep learning approach
to sparse spherical deconvolution, yet it typically does not account for
relationships between neighboring measurements. This work constructs
equivariant deep learning layers which respect to symmetries of spatial
rotations, reflections, and translations, alongside the symmetries of voxelwise
spherical rotations. As a result, RT-ESD improves on previous work across
several tasks including fiber recovery on the DiSCo dataset,
deconvolution-derived partial volume estimation on real-world \textit{in vivo}
human brain dMRI, and improved downstream reconstruction of fiber tractograms
on the Tractometer dataset. Our implementation is available at
https://github.com/AxelElaldi/e3so3_convComment: Accepted to Medical Imaging with Deep Learning (MIDL) 2023. Code
available at https://github.com/AxelElaldi/e3so3_conv . 19 pages with 6
figure
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