TY - JOUR

T1 - Diffusion-weighted MR imaging for the assessment of renal function

T2 - Analysis using statistical models based on truncated gaussian and gamma distributions

AU - Yamada, Kentaro

AU - Shinmoto, Hiroshi

AU - Oshio, Koichi

AU - Ito, Seigo

AU - Kumagai, Hiroo

AU - Kaji, Tatsumi

N1 - Publisher Copyright:
© 2015 Japanese Society for Magnetic Resonance in Medicine.
Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.

PY - 2016/4/11

Y1 - 2016/4/11

N2 - 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 m2): 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/mm2) was performed. Truncated Gaussian and gamma models were compared for goodness of fit. Area fractions for the diffusion coefficient D < 1.0 × 10-3 mm2/s (Frac < 1.0) and D > 3.0 × 10-3 mm2/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.

AB - 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 m2): 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/mm2) was performed. Truncated Gaussian and gamma models were compared for goodness of fit. Area fractions for the diffusion coefficient D < 1.0 × 10-3 mm2/s (Frac < 1.0) and D > 3.0 × 10-3 mm2/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.

KW - Chronic kidney disease

KW - Estimated glomerular filtration rate

KW - Magnetic resonance imaging

KW - Non-Gaussian diffusion

KW - Statistical model

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U2 - 10.2463/mrms.mp.2015-0067

DO - 10.2463/mrms.mp.2015-0067

M3 - Article

C2 - 26701694

AN - SCOPUS:84976869596

VL - 15

SP - 237

EP - 245

JO - Magnetic Resonance in Medical Sciences

JF - Magnetic Resonance in Medical Sciences

SN - 1347-3182

IS - 2

ER -