### Abstract

Purpose: To determine the appropriateness of statistical models using the truncated Gaussian distribution and gamma distribution for diffusion signal decay, and to assess the correlation between the parameters obtained from the statistical models and estimated glomerular filtration rate (eGFR). Methods: Twenty-nine patients with chronic kidney disease and 21 healthy volunteers were included and classified in four groups according to eGFR (ml/min/1.73 m^{2}): group 1 (90 ≤ eGFR, n = 10), group 2 (eGFR 60–90, n = 15), group 3 (eGFR 30–60, n = 17), and group 4 (eGFR <30, n = 8). Diffusion-weighted imaging using five b-values (0, 500, 1000, 1500, and 2000 s/mm^{2}) was performed. Truncated Gaussian and gamma models were compared for goodness of fit. Area fractions for the diffusion coefficient D <1.0 × 10-3 mm^{2}/s (Frac <1.0) and D > 3.0 × 10^{-3} mm^{2}/s (Frac > 3.0) obtained from the statistical model were compared among four groups. Correlation between proposed parameters and conventional apparent diffusion coefficient (ADC) with eGFR was calculated. Results: There was no significant difference in goodness of fit between the truncated Gaussian and gamma models. Frac <1.0 and Frac > 3.0 showed good correlation with eGFR, as did ADC. Comparison between groups 1 and 2 showed that only Frac <1.0 in both distribution models had significant differences. Conclusion: Statistical models yield robust interpretation of diffusion magnetic resonance (MR) signals with relevance to histological changes in the kidney. The parameters of the statistical models, particularly Frac <1.0, strongly correlated with eGFR.

Original language | English |
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Pages (from-to) | 237-245 |

Number of pages | 9 |

Journal | Magnetic Resonance in Medical Sciences |

Volume | 15 |

Issue number | 2 |

DOIs | |

Publication status | Published - 2016 Apr 11 |

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### Keywords

- Chronic kidney disease
- Estimated glomerular filtration rate
- Magnetic resonance imaging
- Non-Gaussian diffusion
- Statistical model

### ASJC Scopus subject areas

- Radiology Nuclear Medicine and imaging

### Cite this

*Magnetic Resonance in Medical Sciences*,

*15*(2), 237-245. https://doi.org/10.2463/mrms.mp.2015-0067