Exploring diffusion across permeable barriers at high gradients. I. Narrow pulse approximation

Grebenkov, Denis S.; Nguyen, Van Dang; Li, Jing-Rebecca

doi:10.1016/j.jmr.2014.07.013

Cited by 30 publications

(23 citation statements)

References 94 publications

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“…This dependence on t d makes hard to give precise estimates of R and D i . We get R = 2.6 ± 1 µm and D i = 0.75 ± 0.15 µm 2 /ms (95% confidence intervals), in agreement with the values found in the literature [55,64].…”

Section: Bi-exponential Model With Decaying ρsupporting

confidence: 91%

The Kärger vs bi-exponential model: Theoretical insights and experimental validations

Moutal

Nilsson

Topgaard

2018

Journal of Magnetic Resonance

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We revise three common models accounting for water exchange in pulsed-gradient spin-echo measurements: a bi-exponential model with time-dependent water fractions, the Kärger model, and a modified Kärger model designed for restricted diffusion, e.g. inside cells. The three models are compared and applied to experimental data from yeast cell suspensions. The Kärger model and the modified Kärger model yield very close results and accurately fit the data. The bi-exponential model, although less rigorous, has a natural physical interpretation and suggests a new experimental modality to estimate the water exchange time.

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Section: Bi-exponential Model With Decaying ρsupporting

confidence: 91%

The Kärger vs bi-exponential model: Theoretical insights and experimental validations

Moutal

Nilsson

Topgaard

2018

Journal of Magnetic Resonance

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“…A new approach by Grebenkov et al (2014), assuming high gradients and narrow pulses, may help to address these issues but current results are only applicable to compartment sizes substantially larger than human cells.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Compartment Models with Permeability for White Matter Microstructure Imaging

Nedjati-Gilani

Schneider

Hall

et al. 2014

Lecture Notes in Computer Science

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A B S T R A C TSome microstructure parameters, such as permeability, remain elusive because mathematical models that express their relationship to the MR signal accurately are intractable. Here, we propose to use computational models learned from simulations to estimate these parameters. We demonstrate the approach in an example which estimates water residence time in brain white matter. The residence time τ i of water inside axons is a potentially important biomarker for white matter pathologies of the human central nervous system, as myelin damage is hypothesised to affect axonal permeability, and thus τ i . We construct a computational model using Monte Carlo simulations and machine learning (specifically here a random forest regressor) in order to learn a mapping between features derived from diffusion weighted MR signals and ground truth microstructure parameters, including τ i . We test our numerical model using simulated and in vivo human brain data. Simulation results show that estimated parameters have strong correlations with the ground truth parameters (R = {0.88, 0.95, 0.82, 0.99} 2 ) for volume fraction, residence time, axon radius and diffusivity respectively), and provide a marked improvement over the most widely used Kärger model (R = {0.75, 0.60, 0.11, 0.99} 2 ). The trained model also estimates sensible microstructure parameters from in vivo human brain data acquired from healthy controls, matching values found in literature, and provides better reproducibility than the Kärger model on both the voxel and ROI level. Finally, we acquire data from two Multiple Sclerosis (MS) patients and compare to the values in healthy subjects. We find that in the splenium of corpus callosum (CC-S) the estimate of the residence time is 0.57 ± 0.05 s for the healthy subjects, while in the MS patient with a lesion in CC-S it is 0.33 ± 0.12 s in the normal appearing white matter (NAWM) and 0.19 ± 0.11 s in the lesion. In the corticospinal tracts (CST) the estimate of the residence time is 0.52 ± 0.09 s for the healthy subjects, while in the MS patient with a lesion in CST it is 0.56 ± 0.05 s in the NAWM and 0.13 ± 0.09 s in the lesion. These results agree with our expectations that the residence time in lesions would be lower than in NAWM because the loss of myelin should increase permeability. Overall, we find parameter estimates in the two MS patients consistent with expectations from the pathology of MS lesions demonstrating the clinical potential of this new technique.

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“…5). In particular, this method generalizes earlier approaches [68,[71][72][73] and opens unprecedented opportunities for studying more sophisticated configurations of barriers such as microstructures inside larger scale structures. The computations are detailed in Sec.…”

Section: Application To Diffusion Mrimentioning

confidence: 65%

Diffusion Across Semi-permeable Barriers: Spectral Properties, Efficient Computation, and Applications

Moutal

2019

J Sci Comput

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We present an efficient method to compute the eigenvalues and eigenmodes of the diffusion operator ∇(D∇) on one-dimensional heterogeneous structures with multiple semi-permeable barriers. This method allows us to calculate the diffusion propagator and related quantities such as diffusion MRI signal or first exit time distribution analytically for regular geometries and numerically for arbitrary ones. The effect of the barriers and the transition from infinite permeability (no barriers) to zero permeability (impermeable barriers) are investigated.

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Exploring diffusion across permeable barriers at high gradients. I. Narrow pulse approximation

Cited by 30 publications

References 94 publications

The Kärger vs bi-exponential model: Theoretical insights and experimental validations

The Kärger vs bi-exponential model: Theoretical insights and experimental validations

Machine Learning Based Compartment Models with Permeability for White Matter Microstructure Imaging

Diffusion Across Semi-permeable Barriers: Spectral Properties, Efficient Computation, and Applications

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