Intravoxel incoherent motion modeling in the kidneys: Comparison of mono-, bi-, and triexponential fit

Baalen, Sophie van; Leemans, Alexander; Dik, Pieter; Lilien, Marc R.; Haken, Bernard ten; Froeling, Martijn

doi:10.1002/jmri.25519

Cited by 57 publications

(101 citation statements)

References 46 publications

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“…It appears more likely that the triexponential behavior originates from a distribution of flow velocities due to the presence of different compartments and of different vessel sizes . This point of view is fortified by the results by Henkelman et al They used perfluorinated hydrocarbon blood substitutes in F rat brain MRI, which allowed measuring solely the perfusion compartment, and ascribed the arising nonexponential signal decay curve to a distribution of flow‐velocities that are naturally present in a tissue that comprises smaller and larger vessels. Albeit this general interpretation, it is still puzzling why S A/V shows such a strong dependency on B 0 , and f 2 / f 1 showed hardly any dependency (see Table ).…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

“…The decision for fitting a triexponential model was made because of recent reports indicating that a triexponential model might be more appropriate. 11,12,[17][18][19] The formula for the biexponential IVIM model reads 20 :…”

Section: Methodsmentioning

confidence: 99%

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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“…It is also worth mentioning that in Federau et al (2015), the value of the TR was 12 times longer than the one used in this study, which affects the T1-weighting of the signal and, hence, may partially explain the observed differences. The acquisition of more diffusion weightings would also allow to employ more sophisticated IVIM fit approaches than the one here used, such as stretched exponentials (Koh, Collins, & Orton, 2011) or proper multiexponential fit (De Luca, Leemans, Bertoldo, Arrigoni, & Froeling, 2018;van Baalen et al, 2017), taking into account the diffusion coefficient of the pseudo-diffusion pool and improving fit quality. When trying to map task activations directly with ADC IVIM -fMRI, we did not find any activation cluster, likely due to insufficient statistical power.…”

Section: Discussionmentioning

confidence: 99%

On the sensitivity of the diffusion MRI signal to brain activity in response to a motor cortex paradigm

Luca

Schlaffke

Siero

et al. 2019

Human Brain Mapping

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Diffusion functional magnetic resonance imaging (dfMRI) is a promising technique to map functional activations by acquiring diffusion‐weighed spin‐echo images. In previous studies, dfMRI showed higher spatial accuracy at activation mapping compared to classic functional MRI approaches. However, it remains unclear whether dfMRI measures result from changes in the intracellular/extracellular environment, perfusion, and/or T2 values. We designed an acquisition/quantification scheme to disentangle such effects in the motor cortex during a finger‐tapping paradigm. dfMRI was acquired at specific diffusion weightings to selectively suppress perfusion and free‐water diffusion, then time series of the apparent diffusion coefficient (ADC‐fMRI) and of intravoxel incoherent motion (IVIM) effects were derived. ADC‐fMRI provided ADC estimates sensitive to changes in perfusion and free‐water volume, but not to T2/T2* values. With IVIM modeling, we isolated the perfusion contribution to ADC, while suppressing T2 effects. Compared to conventional gradient‐echo blood oxygenation level‐dependent fMRI, activation maps obtained with dfMRI and ADC‐fMRI had smaller clusters, and the spatial overlap between the three techniques was below 50%. Increases of perfusion fractions were observed during task in both dfMRI and ADC‐fMRI activations. Perfusion effects were more prominent with ADC‐fMRI than with dfMRI but were significant in less than 25% of activation regions. IVIM modeling suggests that the sensitivity to task of dfMRI derives from a decrease of intracellular/extracellular diffusion and an increase of the pseudo‐diffusion signal fraction, leading to different, more confined spatial activation patterns compared to classic functional MRI.

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“…Precise quantitative diffusion-weighted imaging has the potential to be reliably used in clinic to identify renal pathologies where kidney function is compromised [21, 11, 9, 2]. Our motion-compensated DW-MRI framework can also be used with other signal decay models of kidneys such as combined diffusion tensor-IVIM model [20] or 3-compartment signal decay model [24]. …”

Section: Discussionmentioning

confidence: 99%

Motion-Robust Spatially Constrained Parameter Estimation in Renal Diffusion-Weighted MRI by 3D Motion Tracking and Correction of Sequential Slices

Kurugol

Marami

Afacan

et al. 2017

Lecture Notes in Computer Science

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In this work, we introduce a novel motion-robust spatially constrained parameter estimation (MOSCOPE) technique for kidney diffusion-weighted MRI. The proposed motion compensation technique does not require a navigator, trigger, or breath-hold but only uses the intrinsic features of the acquired data to track and compensate for motion to reconstruct precise models of the renal diffusion signal. We have developed a technique for physiological motion tracking based on robust state estimation and sequential registration of diffusion sensitized slices acquired within 200ms. This allows a sampling rate of 5Hz for state estimation in motion tracking that is sufficiently faster than both respiratory and cardiac motion rates in children and adults, which range between 0.8 to 0.2Hz, and 2.5 to 1Hz, respectively. We then apply the estimated motion parameters to data from each slice and use motion-compensated data for 1) robust intra-voxel incoherent motion (IVIM) model estimation in the kidney using a spatially constrained model fitting approach, and 2) robust weighted least squares estimation of the diffusion tensor model. Experimental results, including precision of IVIM model parameters using bootstrap-sampling and in-vivo whole kidney tractography, showed significant improvement in precision and accuracy of these models using the proposed method compared to models based on the original data and volumetric registration.

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Intravoxel incoherent motion modeling in the kidneys: Comparison of mono-, bi-, and triexponential fit

Cited by 57 publications

References 46 publications

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

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

On the sensitivity of the diffusion MRI signal to brain activity in response to a motor cortex paradigm

Motion-Robust Spatially Constrained Parameter Estimation in Renal Diffusion-Weighted MRI by 3D Motion Tracking and Correction of Sequential Slices

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