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Non‐gaussian diffusion evaluation of the human kidney by Padé exponent model
Author(s) -
Ljimani Alexandra,
Lanzman Rotem S.,
MüllerLutz Anja,
Antoch Gerald,
Wittsack HansJörg
Publication year - 2018
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.25742
Subject(s) - kurtosis , akaike information criterion , diffusion , gaussian , exponent , nuclear magnetic resonance , nuclear medicine , mathematics , physics , biomedical engineering , medicine , statistics , thermodynamics , linguistics , philosophy , quantum mechanics
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 (medullarR bi 20.989 ± 0.008, AICc bi 113.3 ± 6.6; corticalR bi 20.992 ± 0.006, AICc bi 113.3 ± 5.2) than the three‐parameter models (medullarR Pad é 20.965 ± 0.016, AICc Padé 122.6 ± 6.4,R K 20.954 ± 0.019, AICc K 128.5 ± 6.0; corticalR Pad é 20.989 ± 0.005, AICc Padé 116.3 ± 4.4,R K 20.985 ± 0.007, AICc K 120.4 ± 4.8). The monoexponential model fits least to the diffusion signal in the kidney (medullarR mono 20.898 ± 0.039, AICc mono 141.4 ± 5.6; corticalR mono 20.961 ± 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.