283 research outputs found
Maxwell-compensated design of asymmetric gradient waveforms for tensor-valued diffusion encoding
Purpose: Asymmetric gradient waveforms are attractive for diffusion encoding
due to their superior efficiency, however, the asymmetry may cause a residual
gradient moment at the end of the encoding. Depending on the experiment setup,
this residual moment may cause significant signal bias and image artifacts. The
purpose of this study was to develop an asymmetric gradient waveform design for
tensor-valued diffusion encoding that is not affected by concomitant gradient.
Methods: The Maxwell index was proposed as a scalar invariant that captures the
effect of concomitant gradients and was constrained in the numerical
optimization to 100 (mT/m)ms to yield Maxwell-compensated waveforms. The
efficacy of this design was tested in an oil phantom, and in a healthy human
brain. For reference, waveforms from literature were included in the analysis.
Simulations were performed to investigate if the design was valid for a wide
range of experiments and if it could predict the signal bias. Results:
Maxwell-compensated waveforms showed no signal bias in oil or in the brain. By
contrast, several waveforms from literature showed gross signal bias. In the
brain, the bias was large enough to markedly affect both signal and parameter
maps, and the bias could be accurately predicted by theory. Conclusion:
Constraining the Maxwell index in the optimization of asymmetric gradient
waveforms yields efficient tensor-valued encoding with concomitant gradients
that have a negligible effect on the signal. This waveform design is especially
relevant in combination with strong gradients, long encoding times, thick
slices, simultaneous multi-slice acquisition and large/oblique FOVs
Gradient waveform design for tensor-valued encoding in diffusion MRI
Diffusion encoding along multiple spatial directions per signal acquisition
can be described in terms of a b-tensor. The benefit of tensor-valued diffusion
encoding is that it unlocks the "shape of the b-tensor" as a new encoding
dimension. By modulating the b-tensor shape, we can control the sensitivity to
microscopic diffusion anisotropy which can be used as a contrast mechanism; a
feature that is inaccessible by conventional diffusion encoding. Since imaging
methods based on tensor-valued diffusion encoding are finding an increasing
number of applications we are prompted to highlight the challenge of designing
the optimal gradient waveforms for any given application. In this review, we
first establish the basic design objectives in creating field gradient
waveforms for tensor-valued diffusion MRI. We also survey additional design
considerations related to limitations imposed by hardware and physiology,
potential confounding effects that cannot be captured by the b-tensor, and
artifacts related to the diffusion encoding waveform. Throughout, we discuss
the expected compromises and tradeoffs with an aim to establish a more complete
understanding of gradient waveform design and its impact on accurate
measurements and interpretations of data.Comment: Invited review, submitted in May 2020 to the Journal of Neuroscience
Methods. 46 pages, 9 figures, 35 equation
Orientationally-averaged diffusion-attenuated magnetic resonance signal for locally-anisotropic diffusion
Diffusion-attenuated MR signal for heterogeneous media has been represented
as a sum of signals from anisotropic Gaussian sub-domains. Any effect of
macroscopic (global or ensemble) anisotropy in the signal can be removed by
averaging the signal values obtained by differently oriented experimental
schemes. The resulting average signal is identical to what one would get if the
micro-domains are isotropically (e.g., randomly) distributed, which is the case
for "powdered" specimens. We provide exact expressions for the
orientationally-averaged signal obtained via general gradient waveforms when
the microdomains are characterized by a general diffusion tensor possibly
featuring three distinct eigenvalues. Our results are expected to be useful in
not only multidimensional diffusion MR but also solid-state NMR spectroscopy
due to the mathematical similarities in the two fields.Comment: 13 pages (manuscript) + 12 pages (supplementary material), 4 figure
Whole brain resting state functional connectivity abnormalities in schizophrenia
Background
Schizophrenia has been associated with disturbances in brain connectivity; however the exact nature of these disturbances is not fully understood. Measuring temporal correlations between the functional MRI time courses of spatially disparate brain regions obtained during rest has recently emerged as a popular paradigm for estimating brain connectivity. Previous resting state studies in schizophrenia explored connections related to particular clinical or cognitive symptoms (connectivity within a-priori selected networks), or connections restricted to functional networks obtained from resting state analysis. Relatively little has been done to understand global brain connectivity in schizophrenia.
Methods
Eighteen patients with chronic schizophrenia and 18 healthy volunteers underwent a resting state fMRI scan on a 3 T magnet. Whole brain temporal correlations have been estimated using resting-state fMRI data and free surfer cortical parcellations. A multivariate classification method was then used to indentify brain connections that distinguish schizophrenia patients from healthy controls.
Results
The classification procedure achieved a prediction accuracy of 75% in differentiating between groups on the basis of their functional connectivity. Relative to controls, schizophrenia patients exhibited co-existing patterns of increased connectivity between parietal and frontal regions, and decreased connectivity between parietal and temporal regions, and between the temporal cortices bilaterally. The decreased parieto-temporal connectivity was associated with the severity of patients' positive symptoms, while increased fronto-parietal connectivity was associated with patients' negative and general symptoms.
Discussion
Our analysis revealed two co-existing patterns of functional connectivity abnormalities in schizophrenia, each related to different clinical profiles. Such results provide further evidence that abnormalities in brain connectivity, characteristic of schizophrenia, are directly related to the clinical features of the disorder.National Alliance for Medical Image Computing (U.S.) (Grant U54 EB005149)National Institutes of Health (U.S.) (R01 M074794)Medical Research Council of Australia (Overseas-Based Biomedical Traning Fellowship 520627
Co-dimension 2 Geodesic Active Contours for MRA Segmentation
Automatic and semi-automatic magnetic resonance angiography (MRA)s segmentation techniques can potentially save radiologists larges amounts of time required for manual segmentation and cans facilitate further data analysis. The proposed MRAs segmentation method uses a mathematical modeling technique whichs is well-suited to the complicated curve-like structure of bloods vessels. We define the segmentation task as ans energy minimization over all 3D curves and use a level set methods to search for a solution. Ours approach is an extension of previous level set segmentations techniques to higher co-dimension
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Fiber Bundle Estimation and Parameterization
Individual white matter fibers cannot be resolved by current magnetic resonance (MR) technology. Many fibers of a fiber bundle will pass through an individual volume element (voxel). Individual visualized fiber tracts are thus the result of interpolation on a relatively coarse voxel grid, and an infinite number of them may be generated in a given volume by interpolation. This paper aims at creating a level set representation of a fiber bundle to describe this apparent continuum of fibers. It further introduces a coordinate system warped to the fiber bundle geometry, allowing for the definition of geometrically meaningful fiber bundle measures
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