Isotropic diffusion weighting in PGSE NMR by magic-angle spinning of the q-vector

Eriksson, Stefanie; Lasič, Samo; Topgaard, Daniel

doi:10.1016/j.jmr.2012.10.015

Cited by 138 publications

(167 citation statements)

References 57 publications

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“…For brevity, we refer to μ 2 as the variance. In the case of a twocomponent isotropic system, e.g., intra and extracellular diffusion in a yeast cell suspensions [34], the value of μ 2 increases with the difference between the two diffusivities and is maximized when the two contributions are represented with equal probabilities.…”

Section: Frontiers In Physicsmentioning

confidence: 99%

“…For macroscopically isotropic systems, with axially symmetric anisotropic micro-domains, the signal attenuation and the corresponding P(D) can be expressed in a compact form (see Equations 34 and 35 in [34]). The mean diffusivity and the variance are given by the axial and radial diffusivities as…”

Section: Microscopic Fractional Anisotropy (µFa)mentioning

confidence: 99%

“…We have recently shown that microscopic anisotropy can be efficiently detected with an acquisition protocol including singleshot isotropic diffusion weighting (DW) using magic-angle spinning of the q-vector (q-MAS) [34]. Comparisons between the q-MAS and other single-shot DW approaches [35,36] can be found in [37].…”

Section: Introductionmentioning

confidence: 99%

“…Here we implement a numerically optimized version of the q-MAS pulse sequence [37] on a high-performance microimaging system, limited to specimens with maximum 10 mm diameter, and on a standard whole-body clinical scanner. The efficiency of the q-MAS sequence is demonstrated using two materials with pronounced water diffusion anisotropy: lyotropic liquid crystals [24,25,27,34,[38][39][40] and pureed asparagus [41][42][43][44]. For contrast, a yeast cells suspension is used, exhibiting two isotropic diffusion components [34,[45][46][47].…”

Section: Introductionmentioning

confidence: 99%

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Microanisotropy imaging: quantification of microscopic diffusion anisotropy and orientational order parameter by diffusion MRI with magic-angle spinning of the q-vector

et al. 2014

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Diffusion tensor imaging (DTI) is the method of choice for non-invasive investigations of the structure of human brain white matter (WM). The results are conventionally reported as maps of the fractional anisotropy (FA), which is a parameter related to microstructural features such as axon density, diameter, and myelination. The interpretation of FA in terms of microstructure becomes ambiguous when there is a distribution of axon orientations within the image voxel. In this paper, we propose a procedure for resolving this ambiguity by determining a new parameter, the microscopic fractional anisotropy (μFA), which corresponds to the FA without the confounding influence of orientation dispersion. In addition, we suggest a method for measuring the orientational order parameter (OP) for the anisotropic objects. The experimental protocol is capitalizing on a recently developed diffusion nuclear magnetic resonance (NMR) pulse sequence based on magic-angle spinning of the q-vector. Proof-of-principle experiments are carried out on microimaging and clinical MRI equipment using lyotropic liquid crystals and plant tissues as model materials with high μFA and low FA on account of orientation dispersion. We expect the presented method to be especially fruitful in combination with DTI and high angular resolution acquisition protocols for neuroimaging studies of gray and white matter.

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Section: Frontiers In Physicsmentioning

confidence: 99%

Section: Microscopic Fractional Anisotropy (µFa)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Microanisotropy imaging: quantification of microscopic diffusion anisotropy and orientational order parameter by diffusion MRI with magic-angle spinning of the q-vector

et al. 2014

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“…Such methods compare the MR signal acquired with the two blocks either in a parallel or perpendicular orientation, resulting in anisotropic or partially isotropic diffusion encoding, respectively. Lasič et al (2014) proposed an alternative that involved complete instead of partial isotropic encoding, based on magic angle spinning of the q-vector (qMAS; Eriksson et al, 2013). Here, we demonstrate quantification of microscopic anisotropy (mFA) in a healthy volunteer using the qMAS technique, and we elucidate how the measures of anisotropy (mFA and FA) respond to various changes in micro-architecture.…”

Section: Introductionmentioning

confidence: 98%

Microstructures of Learning: Novel methods and approaches for assessing structural and functional changes underlying knowledge acquisition in the brain

Horne

Lindgren²,

Nilsson

et al. 2015

Microstructures of Learning: Novel Methods and Approaches for Assessing Structural and Functional Changes Underlying Knowledge

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Searching for the neurite density with diffusion MRI: Challenges for biophysical modeling

Lampinen

Szczepankiewicz

Novén

et al. 2019

Human Brain Mapping

110

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In vivo mapping of the neurite density with diffusion MRI (dMRI) is a high but challenging aim. First, it is unknown whether all neurites exhibit completely anisotropic (“stick‐like”) diffusion. Second, the “density” of tissue components may be confounded by non‐diffusion properties such as T2 relaxation. Third, the domain of validity for the estimated parameters to serve as indices of neurite density is incompletely explored. We investigated these challenges by acquiring data with “b‐tensor encoding” and multiple echo times in brain regions with low orientation coherence and in white matter lesions. Results showed that microscopic anisotropy from b‐tensor data is associated with myelinated axons but not with dendrites. Furthermore, b‐tensor data together with data acquired for multiple echo times showed that unbiased density estimates in white matter lesions require data‐driven estimates of compartment‐specific T2 values. Finally, the “stick” fractions of different biophysical models could generally not serve as neurite density indices across the healthy brain and white matter lesions, where outcomes of comparisons depended on the choice of constraints. In particular, constraining compartment‐specific T2 values was ambiguous in the healthy brain and had a large impact on estimated values. In summary, estimating neurite density generally requires accounting for different diffusion and/or T2 properties between axons and dendrites. Constrained “index” parameters could be valid within limited domains that should be delineated by future studies.

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Isotropic diffusion weighting in PGSE NMR by magic-angle spinning of the q-vector

Cited by 138 publications

References 57 publications

Microanisotropy imaging: quantification of microscopic diffusion anisotropy and orientational order parameter by diffusion MRI with magic-angle spinning of the q-vector

Microanisotropy imaging: quantification of microscopic diffusion anisotropy and orientational order parameter by diffusion MRI with magic-angle spinning of the q-vector

Microstructures of Learning: Novel methods and approaches for assessing structural and functional changes underlying knowledge acquisition in the brain

Searching for the neurite density with diffusion MRI: Challenges for biophysical modeling

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