PURPOSE: To present a method of automated parametric quantification of dynamic MR enhancement curves of renal transplants and evaluate the disease-discriminating properties of the resulting MR renography (MRR) data. MATERIALS AND METHODS: This study included 27 patients with nondiseased renal transplants and eight patients with diseased renal transplants. The examination was repeated in 10 patients and the reproducibility of the enhancement parameters was estimated by analysis of variance (ANOVA). The disease-discriminating properties of the transplant volumes and enhancement parameters were tested with t-tests and logistic regression analysis. RESULTS: The enhancement parameters were reproducible. The mean medullary nephronal washout rate (lambda1) and cortical arterial blood volume (mu0) were lower in diseased renal transplants. The combination of these parameters was a strong predictor of renal transplant disease (area under ROC curve 0.98; 95% confidence interval 0.96-1.0). CONCLUSION: Automated parametric quantification of cortical and medullary enhancement is feasible and allows the accurate detection of nonsurgical disease in renal transplants by MRR. Copyright 2002 Wiley-Liss, Inc.
PURPOSE: To present a method of automated parametric quantification of dynamic MR enhancement curves of renal transplants and evaluate the disease-discriminating properties of the resulting MR renography (MRR) data. MATERIALS AND METHODS: This study included 27 patients with nondiseased renal transplants and eight patients with diseased renal transplants. The examination was repeated in 10 patients and the reproducibility of the enhancement parameters was estimated by analysis of variance (ANOVA). The disease-discriminating properties of the transplant volumes and enhancement parameters were tested with t-tests and logistic regression analysis. RESULTS: The enhancement parameters were reproducible. The mean medullary nephronal washout rate (lambda1) and cortical arterial blood volume (mu0) were lower in diseased renal transplants. The combination of these parameters was a strong predictor of renal transplant disease (area under ROC curve 0.98; 95% confidence interval 0.96-1.0). CONCLUSION: Automated parametric quantification of cortical and medullary enhancement is feasible and allows the accurate detection of nonsurgical disease in renal transplants by MRR. Copyright 2002 Wiley-Liss, Inc.
Authors: Christine U Lee; James F Glockner; Kevin J Glaser; Meng Yin; Jun Chen; Akira Kawashima; Bohyun Kim; Walter K Kremers; Richard L Ehman; James M Gloor Journal: Acad Radiol Date: 2012-04-13 Impact factor: 3.173
Authors: Akira Yamamoto; Jeff L Zhang; Henry Rusinek; Hersh Chandarana; Pierre-Hugues Vivier; James S Babb; Thomas Diflo; Devon G John; Judith A Benstein; Laura Barisoni; David R Stoffel; Vivian S Lee Journal: Radiology Date: 2011-07-19 Impact factor: 11.105