On modeling

Novikov, Dmitry S.; Kiselev, Valerij G.; Jespersen, Sune Nørhøj

doi:10.1002/mrm.27101

Cited by 332 publications

(405 citation statements)

References 172 publications

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“…In this paper, we proposed a simple technique for estimating T 2a from mono‐exponential fits to direction‐averaged dMRI data for b‐values exceeding about 4000 s/mm 2 . The key idea underlying this method is that the direction‐averaged signal is dominated by the contribution from intra‐axonal water for large b‐values, which is supported by the observed

b^{- 1 false/ 2}

scaling behavior . Our results for T 2a are largely consistent with those obtained by Veraart et al using the TEdDI method, even though TEdDI differs markedly from our approach in being based on a detailed model for both the intra‐axonal and extra‐axonal spaces and in requiring complex nonlinear fitting .…”

Section: Discussionsupporting

confidence: 89%

“…The key idea is that the direction‐averaged dMRI signal is dominated by intra‐axonal water for sufficiently large b‐values and TEs. As discussed in previous studies, b‐values of about 4000 s/mm 2 or higher should be sufficient to suppress most of the signal from extra‐axonal water . The TE need only be large enough to suppress the signal from myelin water.…”

Section: Introductionmentioning

confidence: 90%

“…With increasing b‐value ( b ), the direction‐averaged dMRI signal in white matter has been observed to decay approximately as

b^{- 1 false/ 2}

for b‐values greater than about 4000 s/mm 2 and TEs greater than about 80 ms . This scaling behavior is the signature of water confined to thin cylindrical pores, which presumably correspond to axons.…”

Section: Theorymentioning

confidence: 98%

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Measuring intra‐axonal T₂ in white matter with direction‐averaged diffusion MRI

McKinnon

Jensen

2018

Magnetic Resonance in Med

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Purpose To demonstrate how the T2 relaxation time of intra‐axonal water (T2a) in white matter can be measured with direction‐averaged diffusion MRI. Methods For b‐values larger than about 4000 s/mm2, the direction‐averaged diffusion MRI signal from white matter is dominated by the contribution from water within axons, which enables T2a to be estimated by acquiring data for multiple TE values and fitting a mono‐exponential decay curve. If given a value of the intra‐axonal diffusivity, an extension of the method allows the extra‐axonal relaxation time (T2e) to be calculated also. This approach was applied to estimate T2a in white matter for 3 healthy subjects at 3 T, as well as T2e for a selected set of assumed intra‐axonal diffusivities. Results The estimated T2a values ranged from about 50 ms to 110 ms, with considerable variation among white matter regions. For white matter tracts with primarily collinear fibers, T2a was found to depend on the angle of the tract relative to the main magnetic field, which is consistent with T2a being affected by magnetic field inhomogeneities arising from spatial differences in magnetic susceptibility. The T2e values were significantly smaller than the T2a values across white matter regions for several plausible choices of the intra‐axonal diffusivity. Conclusion The relaxation time for intra‐axonal water in white matter can be determined in a straightforward manner by measuring the direction‐averaged diffusion MRI signal with a large b‐value for multiple TEs. In healthy brain, T2a is greater than T2e and varies considerably with anatomical region.

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b^{- 1 false/ 2}

Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 90%

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Measuring intra‐axonal T₂ in white matter with direction‐averaged diffusion MRI

McKinnon

Jensen

2018

Magnetic Resonance in Med

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“…The ratio f 1 / f 2 in our study did not show the dependency that would be expected if f 1 and f 2 represented portal venous and arterial perfusion compartments, which was hypothesized by Wurnig et al Our results would indicate that the triexponential IVIM “model” should be rather regarded as a triexponential “representation” if one desires to keep the clarifying notion outlined by Novikov et al In this notion, a model represents a biophysical picture; for example, random oriented pipe flow for IVIM . In contrast, a representation is a mere mathematical description of data curves.…”

Section: Discussioncontrasting

confidence: 65%

On the Field Strength Dependence of Bi‐ and Triexponential Intravoxel Incoherent Motion (IVIM) Parameters in the Liver

Riexinger

Martin

Rauh

et al. 2019

Magnetic Resonance Imaging

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Background Studies on intravoxel incoherent motion (IVIM) imaging are carried out with different acquisition protocols. Purpose To investigate the dependence of IVIM parameters on the B0 field strength when using a bi‐ or triexponential model. Study Type Prospective. Study Population 20 healthy volunteers (age: 19–28 years). Field Strength/Sequence Volunteers were examined at two field strengths (1.5 and 3T). Diffusion‐weighted images of the abdomen were acquired at 24 b‐values ranging from 0.2 to 500 s/mm2. Assessment ROIs were manually drawn in the liver. Data were fitted with a bi‐ and a triexponential IVIM model. The resulting parameters were compared between both field strengths. Statistical Tests One‐way analysis of variance (ANOVA) and Kruskal–Wallis test were used to test the obtained IVIM parameters for a significant field strength dependency. Results At b‐values below 6 s/mm2, the triexponential model provided better agreement with the data than the biexponential model. The average tissue diffusivity was D = 1.22/1.00 μm2/msec at 1.5/3T. The average pseudodiffusion coefficients for the biexponential model were D* = 308/260 μm2/msec at 1.5/3T; and for the triexponential model D1* = 81.3/65.9 μm2/msec, D2* = 2453/2333 μm2/msec at 1.5/3T. The average perfusion fractions for the biexponential model were f = 0.286/0.303 at 1.5/3T; and for the triexponential model f1 = 0.161/0.174 and f2 = 0.152/0.159 at 1.5/3T. A significant B0 dependence was only found for the biexponential pseudodiffusion coefficient (ANOVA/KW P = 0.037/0.0453) and tissue diffusivity (ANOVA/KW: P < 0.001). Data Conclusion Our experimental results suggest that triexponential pseudodiffusion coefficients and perfusion fractions obtained at different field strengths could be compared across different studies using different B0. However, it is recommended to take the field strength into account when comparing tissue diffusivities or using the biexponential IVIM model. Considering published values for oxygenation‐dependent transversal relaxation times of blood, it is unlikely that the two blood compartments of the triexponential model represent venous and arterial blood. Level of Evidence: 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:1883–1892.

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“…With respect to the spatial scale, although it is arguable whether the observed anomalous diffusion behaviors of individual molecules can be carried over to an imaging voxel scale, it is plausible that the microscopic diffusion behavior at a molecular level would at least influence the macroscopic diffusion characteristics observed by diffusion MRI. With respect to the temporal scale, it is well known theoretically that the diffusion coefficient approaches zero or infinity in the limit of infinite times . Typical DWI experiments, such as the ones that were performed in our study, however, are conducted in the intermediate‐time regime between 50 and 100 ms.…”

Section: Discussionmentioning

confidence: 99%

A fractional motion diffusion model for a twice‐refocused spin‐echo pulse sequence

Karaman

Zhou

2018

NMR in Biomedicine

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The purpose of this study was to develop an analytical expression for a fractional motion (FM) diffusion model to characterize diffusion-induced signal attenuation in a twice-refocused spin-echo (TRSE) sequence that is resilient to eddy currents, and to demonstrate its applicability to human brain imaging in vivo. Based on the FM theory, which provides a unified statistical description for Langevin motions, the diffusion-weighted (DW) MR signal was measured with a TRSE sequence that balances the concomitant gradients. The analytical expression was fitted to a set of DW images acquired with 14 b-values (0-4000 s/mm ) from a total of 10 healthy human subjects at 3 T, yielding three FM parameter maps based on anomalous diffusion coefficient D , diffusion increment variance φ, and diffusion correlation ψ, respectively. These parameters were used to characterize different brain regions in gray matter (GM), white matter (WM), and cerebrospinal fluid. The analytical expression for the TRSE-based FM model accurately described diffusion signal attenuation in healthy brain tissues at high b-values. TRSE's robustness against eddy currents was illustrated by comparing results from an expression for a conventional Stejskal-Tanner sequence. The TRSE-based FM model also produced consistent GM-WM contrast (p < 0.01) across all brain regions studied, whereas the consistency was not observed with the Stejskal-Tanner-based FM model. This new analytical expression is expected to enable further investigations to probe tissue structures by exploiting anomalous diffusion properties without being hindered by eddy-current perturbations at high b-values.

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On modeling

Cited by 332 publications

References 172 publications

Measuring intra‐axonal T₂ in white matter with direction‐averaged diffusion MRI

Measuring intra‐axonal T₂ in white matter with direction‐averaged diffusion MRI

On the Field Strength Dependence of Bi‐ and Triexponential Intravoxel Incoherent Motion (IVIM) Parameters in the Liver

A fractional motion diffusion model for a twice‐refocused spin‐echo pulse sequence

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On modeling

Cited by 332 publications

References 172 publications

Measuring intra‐axonal T2 in white matter with direction‐averaged diffusion MRI

Measuring intra‐axonal T2 in white matter with direction‐averaged diffusion MRI

On the Field Strength Dependence of Bi‐ and Triexponential Intravoxel Incoherent Motion (IVIM) Parameters in the Liver

A fractional motion diffusion model for a twice‐refocused spin‐echo pulse sequence

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Measuring intra‐axonal T₂ in white matter with direction‐averaged diffusion MRI

Measuring intra‐axonal T₂ in white matter with direction‐averaged diffusion MRI