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• W2610366416 abstract "Purpose To evaluate the feasibility of renal diffusion quantification using the Padé exponent model (PEM) in healthy subjects. Materials and Methods Diffusion measurements were completed in 10 healthy subjects (mean age, 32.4 ± 8.9 years) on a 3T MRI scanner (Magnetom Trio, Siemens AG, Germany). A respiratory‐triggered echo planar imaging sequence (15 slices with 6 mm thickness; 16 b‐values [0–750 s/mm 2 ]; three diffusion directions; field of view: 400 × 375 mm; Matrix 192 × 192; repetition time/echo time: 3000/74 ms) was acquired in the coronal direction. Parameter maps were calculated for the monoexponential, biexponential, kurtosis models, and the PEM. A regression analysis using an R 2 ‐test and corrected Akaike information criterion (AICc) was performed to identify the best mathematical fitting to the measured diffusion‐weighted imaging signal decay. Results The mathematical accuracy of the PEM was significantly higher than for the other three‐parameter and the monoexponential model ( P < 0.05), which enables more precise information about the deviation of the Gaussian behavior of the diffusion signal by the PEM. The biexponential model showed better fitting to the diffusion signal (medullar 0.989 ± 0.008, AICc bi 113.3 ± 6.6; cortical 0.992 ± 0.006, AICc bi 113.3 ± 5.2) than the three‐parameter models (medullar 0.965 ± 0.016, AICc Padé 122.6 ± 6.4, 0.954 ± 0.019, AICc K 128.5 ± 6.0; cortical 0.989 ± 0.005, AICc Padé 116.3 ± 4.4, 0.985 ± 0.007, AICc K 120.4 ± 4.8). The monoexponential model fits least to the diffusion signal in the kidney (medullar 0.898 ± 0.039, AICc mono 141.4 ± 5.6; cortical 0.961 ± 0.013, AICc mono 135.4 ± 4.8). Conclusion The PEM is a novel promising approach to quantify diffusion properties in the human kidney and might further improve functional renal MR imaging. Level of Evidence: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:160–167." @default.
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• W2610366416 date "2017-05-04" @default.
• W2610366416 modified "2023-10-04" @default.
• W2610366416 title "Non-gaussian diffusion evaluation of the human kidney by Padé exponent model" @default.
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• W2610366416 doi "https://doi.org/10.1002/jmri.25742" @default.
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