TY - JOUR
T1 - Intravoxel incoherent motion modeling in the kidneys: Comparison of mono-, bi-, and triexponential fit
AU - van Baalen, Sophie Jacobine
AU - Leemans, Alexander
AU - Dik, Pieter
AU - Lilien, Marc R.
AU - ten Haken, Bernard
AU - Froeling, Martijn
N1 - Open access. Online Version of Record published before inclusion in an issue
PY - 2017/7
Y1 - 2017/7
N2 - Purpose
To 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 Methods
Ten healthy volunteers were examined at 3T, with T2-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 (D1, D2, D3, ffast2, ffast3, and finterm) 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 ( inline image) and the Shapiro-Wilk test was used to test residuals for normality. Maps of diffusion parameters were also visually compared.
Results
Fitting 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 inline image 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 ffast component of the two and three-component models were significantly different (P < 0.001).
Conclusion
Triexponential fitting is feasible for the diffusion signal in the kidney, and provides additional information.
AB - Purpose
To 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 Methods
Ten healthy volunteers were examined at 3T, with T2-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 (D1, D2, D3, ffast2, ffast3, and finterm) 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 ( inline image) and the Shapiro-Wilk test was used to test residuals for normality. Maps of diffusion parameters were also visually compared.
Results
Fitting 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 inline image 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 ffast component of the two and three-component models were significantly different (P < 0.001).
Conclusion
Triexponential fitting is feasible for the diffusion signal in the kidney, and provides additional information.
KW - IR-101881
KW - METIS-318415
U2 - 10.1002/jmri.25519
DO - 10.1002/jmri.25519
M3 - Article
SN - 1053-1807
VL - 46
SP - 228
EP - 239
JO - Journal of magnetic resonance imaging
JF - Journal of magnetic resonance imaging
IS - 1
ER -