<i>D</i>
            : The Diffusion of Water

Wheeler‐Kingshott, Claudia A. M.; Barker, Gareth; Steens, Stefan C. A.; Buchem, Mark A. van

doi:10.1002/0470869526.ch7

Cited by 18 publications

(16 citation statements)

References 114 publications

(159 reference statements)

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“…In biological tissues there are barriers such as cell walls and nerve fibres, and so it is easier for water molecules to diffuse along certain preferred directions. The dominance of the preferred direction of water molecule diffusion can be captured quantitatively using diffusion anisotropy measures [25,39]. Fractional anisotropy (FA) is the most popular such measure [22], defined below as the proportion of the 'magnitude' of D that can be ascribed to anisotropic diffusion:…”

Section: Diffusion Tensor Imagingmentioning

confidence: 99%

Regularisation, interpolation and visualisation of diffusion tensor images using non-Euclidean statistics

Zhou

Dryden

Koloydenko

et al. 2015

Journal of Applied Statistics

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Practical statistical analysis of diffusion tensor images is considered, and we focus primarily on methods that use metrics based on Euclidean distances between powers of diffusion tensors. First we describe a family of anisotropy measures based on a scale invariant power-Euclidean metric, which are useful for visualisation. Some properties of the measures are derived and practical considerations are discussed, with some examples. Second we discuss weighted Procrustes methods for diffusion tensor interpolation and smoothing, and we compare methods based on different metrics on a set of examples as well as analytically. We establish a key relationship between the principal-square-root-Euclidean metric and the size-and-shape Procrustes metric on the space of symmetric positive semi-definite tensors. We explain, both analytically and by experiments, why the size-and-shape Procrustes metric may be preferred in practical tasks of interpolation, extrapolation, and smoothing, especially when observed tensors are degenerate or when a moderate degree of tensor swelling is desirable. Third we introduce regularisation methodology, which is demonstrated to be useful for highlighting features of prior interest and potentially for segmentation. Finally, we compare several metrics in a dataset of human brain diffusion-weighted MRI, and point out similarities between several of the non-Euclidean metrics but important differences with the commonly used Euclidean metric.

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Section: Diffusion Tensor Imagingmentioning

confidence: 99%

Regularisation, interpolation and visualisation of diffusion tensor images using non-Euclidean statistics

Zhou

Dryden

Koloydenko

et al. 2015

Journal of Applied Statistics

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“…DWI allows one to quantify the diffusion behavior of water by estimating the apparent diffusion coefficient (ADC) (Wheeler-Kingshott et al 2003). That is, assuming completely unrestricted motion of water, how is the motion of water impeded by the biological structure of tissue?…”

Section: Diffusion Weighted Imagingmentioning

confidence: 99%

“…DWI is an MR technique that provides a unique insight into tissue structure through MRI diffusion measurements in vivo (Moseley et al 1990;Wheeler-Kingshott et al 2003). These diffusion measurements reflect the effective displacement of water molecules allowed to migrate for a given time (Le Bihan et al 1988).…”

Section: Diffusion Weighted Imagingmentioning

confidence: 99%

“…one may solve for the unknown diffusion to obtain the apparent diffusion coefficient (ADC) D (Wheeler- Kingshott et al 2003). For completeness, S 0 is the (unknown) signal intensity without the diffusion weighting, S is the observed signal with the gradient applied, γ is the gyromagnetic ratio, G is the strength of the gradient pulse, δ is the duration of the gradient pulse and ∆ is the time between the two pulses.…”

Section: Diffusion Weighted Imagingmentioning

confidence: 99%

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Quantitative Analysis of Dynamic Contrast-Enhanced MR Images Based on Bayesian P-Splines

Schmid¹,

Whitcher

Padhani

et al. 2009

IEEE Trans. Med. Imaging

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The package dcemriS4 provides a complete set of data analysis tools for the quantitative assessment of dynamic contrast-enhanced MRI. Image processing is provided for the ANALYZE and NIfTI data formats as inputs with all parameter estimates being output in NIfTI format. Estimation of T1 relaxation from multiple flip-angle acquisitions, using either constant or spatially-varying flip angles, is performed via nonlinear regression. Both literature-based and data-driven arterial input functions are available and may be combined with a variety of compartmental models. Kinetic parameters are obtained from nonlinear regression, Bayesian estimation via Markov Chain Monte Carlo or Bayesian maximum a posteriori estimation. A non-parametric model, using penalized splines, is also available to characterize the contrast agent concentration time curves. Methods for quantification of diffusion-weighted MRI acquisitions common in oncology are also provided. Given the size of multi-dimensional data sets commonly acquired in imaging studies, care has been taken to maximize computational efficiency and minimize memory usage. All methods are illustrated using both simulated and real-world medical imaging data available in the public domain.

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“…It is beyond the scope of this review to discuss details of this technique per se. The interested reader is referred to [41]. Suffice to say here that DWI is capable of depicting regional net H 2 O mobility along a specified direction in the spatial scale of tens of microns.…”

Section: Diffusion Weighted Diffusion Tensor Mri and White Matter Chmentioning

confidence: 99%

The Role of MRI and PET/SPECT in Alzheimers Disease

Coimbra

Williams²,

Hostetler³

2006

CTMC

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Alzheimer's disease (AD) is difficult to diagnose in its early stages, and even if detected early, there is no preventative treatment. Imaging modalities such as MRI, PET, and SPECT have the potential to contribute to both the diagnosis of Alzheimer's disease, as well as assist in the search for more effective treatments. A number of AD-related biomarkers have been proposed and evaluated. The use of PET imaging to detect alterations in regional brain metabolism using [(18)F]FDG has enabled more sensitive and accurate early diagnosis of AD, especially in conjunction with traditional medical evaluation. Additionally, magnetic resonance imaging and spectroscopy provide a wide range of biomarkers that have been shown to correlate with the progression of AD. Some of these markers have been pursued in clinical trials. Progress has been made toward the evaluation of other more AD-specific biomarkers. However, many questions remain concerning the validity and sensitivity of these imaging biomarkers to aid in the assessment of potential new treatments, especially those related to increased levels of amyloid peptides in the brain.

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D : The Diffusion of Water

Cited by 18 publications

References 114 publications

Regularisation, interpolation and visualisation of diffusion tensor images using non-Euclidean statistics

Regularisation, interpolation and visualisation of diffusion tensor images using non-Euclidean statistics

Quantitative Analysis of Dynamic Contrast-Enhanced MR Images Based on Bayesian P-Splines

The Role of MRI and PET/SPECT in Alzheimers Disease

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