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      Optimization of saturation-recovery dynamic contrast-enhanced MRI acquisition protocol: monte carlo simulation approach demonstrated with gadolinium MR renography

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          Abstract

          Dynamic contrast-enhanced (DCE) MRI is widely used for the measurement of tissue perfusion and to assess organ function. MR renography, which is acquired using a DCE sequence, can measure renal perfusion, filtration and concentrating ability. Optimization of the DCE acquisition protocol is important for the minimization of the error propagation from the acquired signals to the estimated parameters, thus improving the precision of the parameters. Critical to the optimization of contrast-enhanced T 1-weighted protocols is the balance of the T 1-shortening effect across the range of gadolinium (Gd) contrast concentration in the tissue of interest. In this study, we demonstrate a Monte Carlo simulation approach for the optimization of DCE MRI, in which a saturation-recovery T 1-weighted gradient echo sequence is simulated and the impact of injected dose ( D) and time delay (TD, for saturation recovery) is tested. The results show that high D and/or high TD cause saturation of the peak arterial signals and lead to an overestimation of renal plasma flow (RPF) and glomerular filtration rate (GFR). However, the use of low TD (e.g. 100 ms) and low D leads to similar errors in RPF and GFR, because of the Rician bias in the pre-contrast arterial signals. Our patient study including 22 human subjects compared TD values of 100 and 300 ms after the injection of 4 mL of Gd contrast for MR renography. At TD = 100 ms, we computed an RPF value of 157.2 ± 51.7 mL/min and a GFR of 33.3 ± 11.6 mL/min. These results were all significantly higher than the parameter estimates at TD = 300 ms: RPF = 143.4 ± 48.8 mL/min ( p = 0.0006) and GFR = 30.2 ± 11.5 mL/min ( p = 0.0015). In conclusion, appropriate optimization of the DCE MRI protocol using simulation can effectively improve the precision and, potentially, the accuracy of the measured parameters.

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          Author and article information

          Journal
          8915233
          1782
          NMR Biomed
          NMR Biomed
          NMR in biomedicine
          0952-3480
          1099-1492
          24 December 2016
          20 May 2016
          July 2016
          03 January 2017
          : 29
          : 7
          : 969-977
          Affiliations
          University of Utah School of Medicine, Department of Radiology, Salt Lake City, UT, USA
          Author notes
          [* ]Correspondence to: J. L. Zhang, University of Utah School of Medicine, Department of Radiology, 729 Arapeend Dr., Salt Lake City, UT 84108, USA. Lei.Zhang@ 123456hsc.utah.edu
          Article
          PMC5206992 PMC5206992 5206992 nihpa838291
          10.1002/nbm.3553
          5206992
          27200499
          00a43bd8-854a-479f-b88d-e6e9ad5c072f
          History
          Categories
          Article

          dynamic contrast-enhanced imaging,MR renography,tracer kinetic modeling,glomerular filtration rate,renal plasma flow

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