NMR microscopy of dynamic displacements: k-space and q-space imaging

Callaghan, Paul T.; Eccles, C.D.; Xia, Yafeng

doi:10.1088/0022-3735/21/8/017

Cited by 278 publications

(220 citation statements)

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“…101,102 Most recently, this strategy has been suggested by Tuch et al for use in clinical scanners, using conventional diffusionweighted images. 89,103 In principle, the potential benefits of using such an approach are numerous: q-space MRI would provide a probability displacement distribution within each voxel in a model-independent way.…”

Section: Dt-mri Findings At`high B-values'mentioning

confidence: 99%

Diffusion‐tensor MRI: theory, experimental design and data analysis – a technical review

2002

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This article treats the theoretical underpinnings of diffusion-tensor magnetic resonance imaging (DT-MRI), as well as experimental design and data analysis issues. We review the mathematical model underlying DT-MRI, discuss the quantitative parameters that are derived from the measured effective diffusion tensor, and describe artifacts thet arise in typical DT-MRI acquisitions. We also discuss difficulties in identifying appropriate models to describe water diffusion in heterogeneous tissues, as well as in interpreting experimental data obtained in such issues. Finally, we describe new statistical methods that have been developed to analyse DT-MRI data, and their potential uses in clinical and multi-site studies.

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Section: Dt-mri Findings At`high B-values'mentioning

confidence: 99%

Diffusion‐tensor MRI: theory, experimental design and data analysis – a technical review

2002

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“…23,24 Measurements of both diffusion and flow can be encoded in a so-called q-space imaging experiment. [25][26][27] In its most common implementation, a standard pulsed-field gradient spin-echo (PGSE) experiment is used. 28 The signal in the PGSE experiment, E, is given by (4) where v is the flow velocity, Δ is the time between the two gradient pulses, D is the diffusion coefficient, and q is defined as (5) where γ is the gyromagnetic ratio of the nuclear spins and g and δ are the amplitude and the width of the gradient pulses, respectively,.…”

Section: Nmr Microimaging Of Flowmentioning

confidence: 99%

Magnetic Resonance Microimaging and Numerical Simulations of Velocity Fields Inside Enlarged Flow Cells Used for Coupled NMR Microseparations

Zhang

Webb

2005

Anal. Chem.

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The coupling of various chemical microseparation methods with small-scale NMR detection is a growing area in analytical chemistry. The formation of enlarged flow cells within the active volume of the NMR detector can significantly increase the coil filling factor and hence the signal-to-noise ratio of the NMR spectra. However, flow cells can also lead to deterioration of the separation efficiency due to the development of complex flow patterns, the form of which depend on the particular geometry of the flow cell and the flow rate used. In this study, we investigated the flow characteristics in different flow cell geometries relevant to the coupling of capillary liquid chromatography and NMR. Computational fluid dynamics was used to simulate fluid flow inside flow cells with a volume of ~ 1 µL. Magnetic resonance microimaging was used to measure experimentally the velocity fields inside these flow cells. The results showed good agreement between experiment and simulation and demonstrated that a relatively gradual expansion and contraction is necessary to avoid areas of weak recirculation and strong radial velocities, both of which can potentially compromise separation efficiency.An essential component in the purification and analysis of unknown compounds is efficient separation of individual components from an often complex chemical or biological system. Advances in microseparations, include techniques such as capillary liquid chromatography (cLC), 1 capillary electrophoresis (CE), 2 and capillary electrochromatography 3 have played a large role in improving the separation efficiency. The chromatographic dilution (D) of cLC, for example, is given by (1) where C 0 is the initial concentration of the analyte, C max is the final compound concentration at the peak maximum, r is the column radius, ϵ is the column porosity, k is the retention factor, L is the column length, H is the column plate height, and V inj is the injected sample volume. Equation 1 shows that a smaller column radius results in less chromatographic dilution and higher concentration elution peaks. The smaller scale is also much more amenable to very rapid separations. In addition to these general benefits of microseparations, in electrophoretic separations the high surface area-to-volume ratio of the small capillaries used in CE systems,

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“…For example, in brain tumor patients, presurgical DTI tractography fails to delineate the course of the pyramidal tract in regions of fiber crossing (10). Although intravoxel diffusion in these areas of complex WM can be more accurately characterized using the qspace formalism (11), long experiment times and heavy gradient demands preclude routine in vivo application of this technique in humans. High angular resolution diffusion imaging (HARDI) represents an alternative approach that strives to improve imaging efficiency by recovering the angular structure of diffusion in lieu of the 3D spindisplacement probability function.…”

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confidence: 99%

Q‐ball reconstruction of multimodal fiber orientations using the spherical harmonic basis

Hess

Mukherjee

Han

et al. 2006

Magnetic Resonance in Med

335

368

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The past decade has seen rapid advances in the development of diffusion MRI techniques that permit for the first time the noninvasive characterization of neural architecture. While the exact biophysical determinants of the diffusion signal have yet to be completely elucidated, it is now generally accepted that microscopic boundaries to diffusion in the brain coincide with the local orientations of white matter (WM) fiber tracts (1). The advent of diffusion tensor imaging (DTI) as a tool for modeling intravoxel diffusion has inspired a number of promising applications in which WM connectivity can be evaluated in both health and disease (2,3). Measures derived from the tensor are now widely used to characterize regional anisotropy and orientation of WM throughout the brain, and more recently have been incorporated into tractography algorithms to allow 3D delineation of fiber pathways (4 -6).A major shortcoming of DTI lies in its inability to accurately characterize diffusion in complex WM, where fiber tracts with different orientations intersect or are otherwise partial volume averaged within a voxel (7-9). This limitation presents a significant obstacle to routine clinical application of DTI. For example, in brain tumor patients, presurgical DTI tractography fails to delineate the course of the pyramidal tract in regions of fiber crossing (10). Although intravoxel diffusion in these areas of complex WM can be more accurately characterized using the qspace formalism (11), long experiment times and heavy gradient demands preclude routine in vivo application of this technique in humans. High angular resolution diffusion imaging (HARDI) represents an alternative approach that strives to improve imaging efficiency by recovering the angular structure of diffusion in lieu of the 3D spindisplacement probability function. Shorter imaging times make HARDI particularly promising for clinical applications, especially when combined with the greater signalto-noise ratios (SNRs) afforded by high-field MR systems (12) and the reduction in single-shot echo-planar image artifacts provided by parallel imaging (12)(13)(14).Several approaches for reconstructing fiber orientations from HARDI data have been proposed, including spherical harmonic modeling of the apparent diffusion coefficient (ADC) profile (15,16), multitensor modeling (17), generalized tensor representations (18,19), circular spectrum mapping (20), q-ball imaging (QBI) (21-24), persistent angular structure (25), and spherical deconvolution (26). Among these techniques, QBI and spherical deconvolution have generated considerable interest due to their linearity and sensitivity to multimodal diffusion. QBI has the additional desirable property of model independence because it does not require ad hoc measurement of a "response function" in order to reconstruct fiber orientations. While the two methods differ significantly in their approach to recovering angular information from the measured diffusion data, both define a continuous function over the sphere that encodes the ...

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NMR microscopy of dynamic displacements: k-space and q-space imaging

Cited by 278 publications

References 14 publications

Diffusion‐tensor MRI: theory, experimental design and data analysis – a technical review

Diffusion‐tensor MRI: theory, experimental design and data analysis – a technical review

Magnetic Resonance Microimaging and Numerical Simulations of Velocity Fields Inside Enlarged Flow Cells Used for Coupled NMR Microseparations

Q‐ball reconstruction of multimodal fiber orientations using the spherical harmonic basis

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