Diffusion-weighted MR imaging for the assessment of renal function: Analysis using statistical models based on truncated gaussian and gamma distributions

Kentaro Yamada, Hiroshi Shinmoto, Koichi Oshio, Seigo Ito, Hiroo Kumagai, Tatsumi Kaji

研究成果: Article

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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.

元の言語English
ページ(範囲)237-245
ページ数9
ジャーナルMagnetic Resonance in Medical Sciences
15
発行部数2
DOI
出版物ステータスPublished - 2016 4 11

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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