Spin Echo Analysis of Restricted Diffusion under Generalized Gradient Waveforms: Planar, Cylindrical, and Spherical Pores with Wall Relaxivity

Codd, Sarah L.; Callaghan, P. T.

doi:10.1006/jmre.1998.1679

Cited by 101 publications

(94 citation statements)

References 21 publications

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“…• Measurement of translational diffusion of the confined water using pulsed magnetic field gradient or fringe magnetic field gradient techniques (Mitzithras et al, 1992;Codd and Callaghan, 1999;Stallmach and Kärger, 1999;Paoli et al, 2002;Kimmich, 2002;Callaghan et al, 2003;Valiullin et al, 2003;Geier et al, 2004).…”

Section: Nmr Relaxation Diffusion and Cryoporometry Studiesmentioning

confidence: 99%

Structural and dynamic studies of water in mesoporous silicas using neutron scattering and nuclear magnetic resonance

Webber

Dore

2004

J. Phys.: Condens. Matter

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Abstract. Experimental techniques for studying the behaviour of water in confined geometry using neutron scattering and NMR methods are reviewed. A brief survey is given of earlier work on sol-gel silicas and these findings are updated by reference to current work on MCM-and SBA-type silicas. Particular attention is focused on the phase transitions and the relation between super-cooled water and ice phases in the confined geometry of the mesopores. The characteristics are found to behave in a systematic manner although the nucleation phenomena for partially-filled pores reveals some unexpected complexity for the SBA-silicas.

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Section: Nmr Relaxation Diffusion and Cryoporometry Studiesmentioning

confidence: 99%

Structural and dynamic studies of water in mesoporous silicas using neutron scattering and nuclear magnetic resonance

Webber

Dore

2004

J. Phys.: Condens. Matter

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“…The analytical expressions used to calculate signals of white matter tissue assume that the water molecules are completely restricted and thus ignore or use simplistic models of permeability (Van Gelderen et al, 1994;Callaghan, 1997;Codd and Callaghan, 1999). 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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“…In sharp contrast with bulk relaxation which acts uniformly over the sample and can, thus, be factored out, surface relaxation affects the statistics of Brownian trajectories, eliminating those which encounter the boundary more frequently. These effects were intensively studied [e.g., (50,(59)(60)(61)(62); see also reviews (2, 7) and references therein].…”

Section: Surface Relaxationmentioning

confidence: 99%

“…2. For simple confining domains (such as slab, cylinder, sphere, cylindrical, and spherical shells), the computation of the macroscopic signal by using matrix formalisms is easy, rapid, and very accurate (2,47,50,62,(71)(72)(73)(74)(75)(76). Unknown parameters (such as the compartment size or free diffusion coefficient) can be extracted by fitting experimental data to numerical curves.…”

Section: Can One Go Beyond Adc?mentioning

confidence: 99%

Use, misuse, and abuse of apparent diffusion coefficients

Grebenkov

2010

Concepts Magnetic Resonance

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ABSTRACT:The notion of effective, time-dependent or, equivalently, apparent diffusion coefficient (ADC) and its use for characterization of porous media are revisited. It is argued that the dynamic ADC, quantifying the mean-square displacement of spin-bearing particles, should not be confused with its counterpart measured by a pulsed gradient spin echo technique. The former is an intrinsic characteristic of the medium, independent of the applied magnetic field. In contrast, the spin-echo ADC depends on the experimental setup (e.g., gradient intensity and temporal profile), raising potential ambiguities in the interpretation of diffusion-weighted measurements, which may be strongly misleading when the Gaussian phase approximation (GPA) does not hold. The oversimplified use of a single b-value is criticized. Several fitting models beyond the GPA are discussed.

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Spin Echo Analysis of Restricted Diffusion under Generalized Gradient Waveforms: Planar, Cylindrical, and Spherical Pores with Wall Relaxivity

Cited by 101 publications

References 21 publications

Structural and dynamic studies of water in mesoporous silicas using neutron scattering and nuclear magnetic resonance

Structural and dynamic studies of water in mesoporous silicas using neutron scattering and nuclear magnetic resonance

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

Use, misuse, and abuse of apparent diffusion coefficients

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