Fundamentals of diffusion MRI physics

Kiselev, Valerij G.

doi:10.1002/nbm.3602

Cited by 102 publications

(113 citation statements)

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“…To place our approach into broader context, diffusion in tissue microstructure is generally non‐Gaussian. As reviewed in detail in the theoretical overview by Kiselev earlier in this issue , any microstructural heterogeneity results both in higher‐order diffusion cumulants (such as kurtosis and beyond), and in their time dependence. In particular, any time dependence in D ( t ) necessarily leads to the presence of time‐dependent kurtosis and higher‐order diffusion metrics .…”

Section: Random Permeable Barrier Modelmentioning

confidence: 99%

In vivo measurement of membrane permeability and myofiber size in human muscle using time‐dependent diffusion tensor imaging and the random permeable barrier model

et al. 2016

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The time dependence of the diffusion coefficient is a hallmark of tissue complexity at the micrometer level. Here we demonstrate how biophysical modeling, combined with a specifically tailored diffusion MRI acquisition performing diffusion tensor imaging (DTI) for varying diffusion times, can be used to determine fiber size and membrane permeability of muscle fibers in vivo. We describe the random permeable barrier model (RPBM) and its assumptions, as well as the details of stimulated echo DTI acquisition, signal processing steps, and potential pitfalls. We illustrate the RPBM method on a few pilot examples involving human subjects (previously published as well as new), such as revealing myofiber size derived from RPBM increase after training in a calf muscle, and size decrease with atrophy in shoulder rotator cuff muscle. Finally, we comment on the potential clinical relevance of our results. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Random Permeable Barrier Modelmentioning

confidence: 99%

In vivo measurement of membrane permeability and myofiber size in human muscle using time‐dependent diffusion tensor imaging and the random permeable barrier model

et al. 2016

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“…It is also noteworthy that fitting the kurtosis tensor greatly improves the accuracy of the diffusion tensor estimation (Veraart et al, 2011). Extending the series to the sixth order cumulant (in b 3 ) increases the accuracy of the kurtosis estimation, albeit with a penalty on precision (Kiselev, 2017). …”

Section: Modelsmentioning

confidence: 99%

“…Moreover, the estimated distribution does not mirror the actual distribution of diffusion coefficients in the tissue unless the measurement is performed in a very strong diffusion weighting regime ( ql c ≫ 1, where q is the amount of spatial phase-warping introduced and l c is the diffusion distance) (Kiselev, 2017; Novikov and Kiselev, 2010). Thus, this approach remains an empirical description of the diffusion-weighted signal and falls in the category of signal representations rather than biophysical models.…”

Section: Modelsmentioning

confidence: 99%

“…A comprehensive discussion on the implications of such a theory for biological tissue can be found in (Kiselev, 2017; Novikov et al, 2016a). Although devoid of biophysical meaning, the stretching parameter α is largely used in the literature for tumor delineation and characterization (Bai et al, 2016; Lai et al, 2015; Liu et al, 2015; Mazaheri et al, 2012).…”

Section: Modelsmentioning

confidence: 99%

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Design and Validation of Diffusion MRI Models of White Matter

2017

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Diffusion MRI is arguably the method of choice for characterizing white matter microstructure in vivo. Over the typical duration of diffusion encoding, the displacement of water molecules is conveniently on a length scale similar to that of the underlying cellular structures. Moreover, water molecules in white matter are largely compartmentalized which enables biologically-inspired compartmental diffusion models to characterize and quantify the true biological microstructure. A plethora of white matter models have been proposed. However, overparameterization and mathematical fitting complications encourage the introduction of simplifying assumptions that vary between different approaches. These choices impact the quantitative estimation of model parameters with potential detriments to their biological accuracy and promised specificity. First, we review biophysical white matter models in use and recapitulate their underlying assumptions and realms of applicability. Second, we present up-to-date efforts to validate parameters estimated from biophysical models. Simulations and dedicated phantoms are useful in assessing the performance of models when the ground truth is known. However, the biggest challenge remains the validation of the “biological accuracy” of estimated parameters. Complementary techniques such as microscopy of fixed tissue specimens have facilitated direct comparisons of estimates of white matter fiber orientation and densities. However, validation of compartmental diffusivities remains challenging, and complementary MRI-based techniques such as alternative diffusion encodings, compartment-specific contrast agents and metabolites have been used to validate diffusion models. Finally, white matter injury and disease pose additional challenges to modeling, which are also discussed. This review aims to provide an overview of the current state of models and their validation and to stimulate further research in the field to solve the remaining open questions and converge towards consensus.

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“…For the PGSE experiment in the narrow pulse regime, the diffusion time t equates the inter-gradient duration . Finite pulse widths δ act as low-pass filter on the velocity autocorrelation function [38,39], potentially impacting the functional form of the diffusion time-dependence (see for instance Equation 8 vs. Equation 9 in Fieremans et al [40]-an axon study).…”

Section: Diffusion Inside Impermeable Spheresmentioning

confidence: 99%

Time-Dependent Diffusion MRI in Cancer: Tissue Modeling and Applications

Reynaud

2017

Front. Phys.

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In diffusion weighted imaging (DWI), the apparent diffusion coefficient (ADC) has been recognized as a useful and sensitive surrogate for cell density, paving the way for non-invasive tumor staging, and characterization of treatment efficacy in cancer. However, microstructural parameters, such as cell size, density and/or compartmental diffusivities affect diffusion in various fashions, making of conventional DWI a sensitive but non-specific probe into changes happening at cellular level. Alternatively, tissue complexity can be probed and quantified using the time dependence of diffusion metrics, sometimes also referred to as temporal diffusion spectroscopy when only using oscillating diffusion gradients. Time-dependent diffusion (TDD) is emerging as a strong candidate for specific and non-invasive tumor characterization. Despite the lack of a general analytical solution for all diffusion times/frequencies, TDD can be probed in various regimes where systems simplify in order to extract relevant information about tissue microstructure. The fundamentals of TDD are first reviewed (a) in the short time regime, disentangling structural and diffusive tissue properties, and (b) near the tortuosity limit, assuming weakly heterogeneous media near infinitely long diffusion times. Focusing on cell bodies (as opposed to neuronal tracts), a simple but realistic model for intracellular diffusion can offer precious insight on diffusion inside biological systems, at all times. Based on this approach, the main three geometrical models implemented so far (IMPULSED, POMACE, VERDICT) are reviewed. Their suitability to quantify cell size, intra-and extracellular spaces (ICS and ECS) and diffusivities are assessed. The proper modeling of tissue membrane permeability-hardly a newcomer in the field, but lacking applications-and its impact on microstructural estimates are also considered. After discussing general issues with tissue modeling and microstructural parameter estimation (i.e., fitting), potential solutions are detailed. The in vivo applications of this new, non-invasive, specific approach in cancer are reviewed, ranging from the characterization of gliomas in rodent brains and observation of time-dependence in breast tissue lesions and prostate cancer, to the recent preclinical evaluation of new treatments efficacy. It is expected that clinical applications of TDD will strongly benefit the community in terms of non-invasive cancer screening.

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Fundamentals of diffusion MRI physics

Cited by 102 publications

References 102 publications

In vivo measurement of membrane permeability and myofiber size in human muscle using time‐dependent diffusion tensor imaging and the random permeable barrier model

In vivo measurement of membrane permeability and myofiber size in human muscle using time‐dependent diffusion tensor imaging and the random permeable barrier model

Design and Validation of Diffusion MRI Models of White Matter

Time-Dependent Diffusion MRI in Cancer: Tissue Modeling and Applications

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