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Markus Nilsson

Researcher at Lund University

Publications -  159
Citations -  5764

Markus Nilsson is an academic researcher from Lund University. The author has contributed to research in topics: Diffusion MRI & Fractional anisotropy. The author has an hindex of 41, co-authored 140 publications receiving 4482 citations. Previous affiliations of Markus Nilsson include University of Florence & University of Coimbra.

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Imaging brain microstructure with diffusion MRI: practicality and applications

TL;DR: The article summarizes the relevant aspects of brain microanatomy and the range of diffusion‐weighted MR measurements that provide to them and reviews the evolution of mathematical and computational models that relate the diffusion MR signal to brain tissue microstructure.
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q-space trajectory imaging for multidimensional diffusion MRI of the human brain.

TL;DR: A microstructure model, the diffusion tensor distribution (DTD) model, is proposed, which takes advantage of additional information provided by QTI to estimate a distributional model over diffusion tensors.
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Quantification of microscopic diffusion anisotropy disentangles effects of orientation dispersion from microstructure: applications in healthy volunteers and in brain tumors

TL;DR: It is suggested that the μFA and OP may complement FA by independently quantifying the microscopic anisotropy and the level of orientation coherence, which is a major limitation to the use of FA as a biomarker for "tissue integrity" in regions of complex microarchitecture.
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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

TL;DR: In this paper, a new parameter called microscopic fractional anisotropy (µFA) was proposed, which corresponds to the FA without the confounding influence of orientation dispersion.
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Neurite density imaging versus imaging of microscopic anisotropy in diffusion MRI : A model comparison using spherical tensor encoding

TL;DR: The ‘constrained diffusional variance decomposition’ (CODIVIDE) method is presented, which jointly analyzes data acquired with diffusion encoding applied in a single direction at a time and in all directions, and concludes that accurate mapping of microscopic anisotropy requiresData acquired with variable shape of the b‐tensor.