PURPOSE: To compare six methods for calculating the single-kidney glomerular filtration rate (GFR) from T(1)-weighted magnetic resonance (MR) renography (MRR) against reference radionuclide measurements. MATERIALS AND METHODS: In 10 patients, GFR was determined using six published methods: the Baumann-Rudin model (BR), the Patlak-Rutland method (PR), the two-compartment model without bolus dispersion (2C) and with dispersion (2CD), the three-compartment model (3CD), and the distributed parameter model (3C-IRF). Reference single-kidney GFRs were measured by radionuclide renography. The coefficient of variation of GFR (CV) was determined for each method by Monte Carlo analyses for one healthy and one dysfunctional kidney at a noise level (sigma(n)) of 2%, 5%, and 10%. RESULTS: GFR estimates in patients varied from 6% overestimation (BR) to 50% underestimation (PR and 2CD applied to cortical data). Correlations with reference GFRs ranged from R = 0.74 (2CD, cortical data) to R = 0.85 (BR). In simulations, the lowest CV was produced by 3C-IRF in healthy kidney (1.7sigma(n)) and by PR in diseased kidney ((2.2-2.4)sigma(n)). In both kidneys the highest CV was obtained with 2CD ((5.9-8.2)sigma(n)) and with 3CD in diseased kidney (8.9sigma(n) at sigma(n) = 10%). CONCLUSION: GFR estimates depend on the renal model and type of data used. Two- and three-compartment models produce comparable GFR correlations.
PURPOSE: To compare six methods for calculating the single-kidney glomerular filtration rate (GFR) from T(1)-weighted magnetic resonance (MR) renography (MRR) against reference radionuclide measurements. MATERIALS AND METHODS: In 10 patients, GFR was determined using six published methods: the Baumann-Rudin model (BR), the Patlak-Rutland method (PR), the two-compartment model without bolus dispersion (2C) and with dispersion (2CD), the three-compartment model (3CD), and the distributed parameter model (3C-IRF). Reference single-kidney GFRs were measured by radionuclide renography. The coefficient of variation of GFR (CV) was determined for each method by Monte Carlo analyses for one healthy and one dysfunctional kidney at a noise level (sigma(n)) of 2%, 5%, and 10%. RESULTS: GFR estimates in patients varied from 6% overestimation (BR) to 50% underestimation (PR and 2CD applied to cortical data). Correlations with reference GFRs ranged from R = 0.74 (2CD, cortical data) to R = 0.85 (BR). In simulations, the lowest CV was produced by 3C-IRF in healthy kidney (1.7sigma(n)) and by PR in diseased kidney ((2.2-2.4)sigma(n)). In both kidneys the highest CV was obtained with 2CD ((5.9-8.2)sigma(n)) and with 3CD in diseased kidney (8.9sigma(n) at sigma(n) = 10%). CONCLUSION: GFR estimates depend on the renal model and type of data used. Two- and three-compartment models produce comparable GFR correlations.
Authors: L Hermoye; L Annet; Ph Lemmerling; F Peeters; F Jamar; P Gianello; S Van Huffel; B E Van Beers Journal: Magn Reson Med Date: 2004-05 Impact factor: 4.668
Authors: Henrik J Michaely; Stefan O Schoenberg; Niels Oesingmann; Carina Ittrich; Christopher Buhlig; Denise Friedrich; Anja Struwe; Johannes Rieger; Cornelia Reininger; Walter Samtleben; Max Weiss; Maximilian F Reiser Journal: Radiology Date: 2006-02 Impact factor: 11.105
Authors: Sila Kurugol; Onur Afacan; Richard S Lee; Catherine M Seager; Michael A Ferguson; Deborah R Stein; Reid C Nichols; Monet Dugan; Alto Stemmer; Simon K Warfield; Jeanne S Chow Journal: Pediatr Radiol Date: 2020-01-27