Intravoxel Incoherent Motion and Diffusion-Tensor Imaging in Renal Tissue under Hydration and Furosemide Flow Challenges

Sigmund, Eric E.; Vivier, P.-H.; Sui, Dabang; Lamparello, Nicole A.; Tantillo, Kristopher; Mikheev, Artem; Rusinek, Henry; Babb, James S.; Storey, Pippa; Lee, Vivian S.; Chandarana, Hersh

doi:10.1148/radiol.12111327

Cited by 195 publications

(256 citation statements)

References 57 publications

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“…Additionally, we showed that the f fast2 and f fast3 from the two‐ and three‐component models showed a high and significant correlation with FA from the DTI model. DTI and IVIM are well‐established fitting methods that have been applied in numerous diffusion MRI studies of the kidneys with consistent results,5, 6, 7, 8, 9, 11, 16, 26, 27, 28, 33, 43 which are in line with our results.…”

Section: Discussionsupporting

confidence: 91%

“…The tractography‐based parameters of all models are most similar to those of the medulla, which is in agreement with the widely accepted belief that diffusion anisotropy originates in the radially oriented structures in the medulla 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 27. However, tract‐based FA is significantly higher than ROI‐based FA in the medulla.…”

Section: Discussionsupporting

confidence: 85%

“…These differences are in part a consequence of the use of different acquisition or processing protocols, for example, the b ‐values used,9, 24 or of using different fitting algorithms 3, 25. However, there might also be physiological causes for this variability; for example, pseudodiffusion was found to correlate with perfusion in an electrocardiogram (ECG)‐triggered time‐resolved study of healthy kidneys,26 while IVIM‐derived parameters were also shown to be sensitive to diuretic challenges 27. This has led to the belief that pseudodiffusion in the kidney consists of a perfusion and a tubular, or free water flow, component 3, 28…”

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confidence: 99%

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

Baalen

Leemans

Dik

et al. 2016

J. Magn. Reson. Imaging

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PurposeTo evaluate if a three‐component model correctly describes the diffusion signal in the kidney and whether it can provide complementary anatomical or physiological information about the underlying tissue.Materials and MethodsTen healthy volunteers were examined at 3T, with T 2‐weighted imaging, diffusion tensor imaging (DTI), and intravoxel incoherent motion (IVIM). Diffusion tensor parameters (mean diffusivity [MD] and fractional anisotropy [FA]) were obtained by iterative weighted linear least squares fitting of the DTI data and mono‐, bi‐, and triexponential fit parameters (D 1, D 2, D 3, f fast2, f fast3, and f interm) using a nonlinear fit of the IVIM data. Average parameters were calculated for three regions of interest (ROIs) (cortex, medulla, and rest) and from fiber tractography. Goodness of fit was assessed with adjusted R2 ( normalRadj2) and the Shapiro‐Wilk test was used to test residuals for normality. Maps of diffusion parameters were also visually compared.ResultsFitting the diffusion signal was feasible for all models. The three‐component model was best able to describe fast signal decay at low b values (b < 50), which was most apparent in normalRadj2 of the ROI containing high diffusion signals (ROIrest), which was 0.42 ± 0.14, 0.61 ± 0.11, 0.77 ± 0.09, and 0.81 ± 0.08 for DTI, one‐, two‐, and three‐component models, respectively, and in visual comparison of the fitted and measured S0. None of the models showed significant differences (P > 0.05) between the diffusion constant of the medulla and cortex, whereas the f fast component of the two and three‐component models were significantly different (P < 0.001).ConclusionTriexponential fitting is feasible for the diffusion signal in the kidney, and provides additional information. Level of Evidence: 2 Technical Efficacy: Stage 1J. MAGN. RESON. IMAGING 2017;46:228–239

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Section: Discussionsupporting

confidence: 91%

Section: Discussionsupporting

confidence: 85%

mentioning

confidence: 99%

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

Baalen

Leemans

Dik

et al. 2016

J. Magn. Reson. Imaging

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“…Good reproducibility of the diffusion parameters (average coefficient of variation [CV] of 3.7 % for cortex and of 5.0 % for the medulla) was reported by Sigmund et al [11]. Wittsack et al [12] reported a dependence of ADC and f p values from the phase of the cardiac cycle proving the influence of the pulsatile blood flow on the evaluation of the parameters in kidneys.…”

Section: Applicationsmentioning

confidence: 87%

“…Hueper et al [18] Yu et al [15] Inci et al [14] Chandarana et al [16] Zhang et al [13] Lu et al [20] Sigmund et al [11] Wittsack et al [12] Rosenkrantz et al [54] Hueper et al [21] ASL Artz et al [26] Pedrosa et al [28] Lanzman et al [29] Rossi et al [27] Cutajar et al [23] Wu et al [24] Boss et al [22] Schor-Bardach et al [30] BOLD Park et al [37] Xiao et al [36] Min et al [38] Yin et al [34] Michaely et al [43] Xin-Long et al [33] Park et al [37] Donati et al [42] Simon-Zoula et al [32] Zhang et al…”

Section: Animal Study Dwi/ Dtimentioning

confidence: 99%

Clinical Functional MRI of the kidneys

2013

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In functional renal magnetic resonance imaging (MRI), advanced techniques are applied to obtain information on a functional and molecular level from the kidney tissue beyond pure morphology. Techniques such as diffusion-weighted and diffusion tensor imaging, arterial spin labelling, and blood oxygenation level-dependent imaging provide potential biomarkers of organ function. Moreover, dynamic contrast-enhanced techniques after the intravenous injection of gadolinium-chelates may be used to assess glomerular filtration and urinary excretion. This review summarizes recent developments of contrast-and non-contrast-enhanced MRI techniques for assessment of renal function in a clinical setting. The physiological background and the sequence techniques are described in detail. Potential clinical applications of the different techniques are discussed regarding their potential usefulness in the assessment of parenchymal diseases, urinary tract anomalies, transplant kidney function, and renal masses.

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Optimization of intra‐voxel incoherent motion measurement in diffusion‐weighted imaging of breast cancer

Chen

Zhang

Long

et al. 2017

J Applied Clin Med Phys

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PurposeThe purpose of this study was to optimize intra‐voxel incoherent motion (IVIM) measurement in diffusion‐weighted imaging (DWI) of breast cancer by separating perfusion and diffusion effects through the determination of an optimal threshold b‐value, thus benign and cancerous breast tissues can be accurately differentiated using IVIM‐derived diffusion and perfusion parameters.Materials and MethodsTwenty‐eight patients, with biopsy‐confirmed breast cancers, were studied with a 3T MRI scanner, using T1‐weighted dynamic contrast‐enhanced MRI images, and diffusion‐weighted images with nine b‐values, ranging from 0 to 1000 s/mm². IVIM‐derived parameter maps for tissue diffusion coefficients D, perfusion fraction f, and pseudo‐diffusion coefficients D* were computed using the segmented fitting method with optimized threshold b‐value, and the sum of squared residuals (SSR) were calculated for IVIM‐derived parameters in different breast lesions.ResultsThe IVIM analysis method developed in this work can separate perfusion and diffusion effects with the optimal threshold b‐value of 300 s/mm², and the results of diffusion and perfusion parameters from IVIM analysis can be used to differentiate pathological changes in breast tissues. It was found that the averages and standard deviations of the diffusion and perfusion parameters, D, f, D*, are the following, for malignant, benign and normal breast tissues respectively: D (0.813 ± 0.225 × 10−3 mm2/s, 1.437 ± 0.538 × 10−3 mm2/s, 1.838 ± 0.213 × 10−3 mm2/s), f (10.73 ± 3.44%, 7.86 ± 3.70%, 8.92 ± 3.72%), D* (15.23 ± 12.17×10−3 mm²/s, 12.02 ± 3.19 × 10−3 mm2/s, 12.03 ± 7.21 × 10−3 mm2/s).Conclusion IVIM‐derived diffusion and perfusion parameter maps depend highly on the choice of threshold b‐value. Using the methodology developed in this work, and with the optimized threshold b‐value, the diffusion and perfusion parameters of breast tissues can be accurately assessed, making IVIM MRI a technique of choice for differential diagnosis of breast cancer.

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Intravoxel Incoherent Motion and Diffusion-Tensor Imaging in Renal Tissue under Hydration and Furosemide Flow Challenges

Cited by 195 publications

References 57 publications

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

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

Clinical Functional MRI of the kidneys

Optimization of intra‐voxel incoherent motion measurement in diffusion‐weighted imaging of breast cancer

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