Abstract
The treatment of chronic renal diseases usually involves the estimation of the glomerular filtration rate (GFR). The GFR can be estimated in vivo without blood samples by pharmacokinetic methods. These models employ non-linear curve fitting techniques to obtain model parameters fitting the model to concentration curves extracted from 4D DCE-MRI data. However, currently proposed optimization strategies rely on the choice of the initial values. In this paper, we propose an improved optimization algorithm based on the analytical elimination of half of the parameters of the Sourbron model. This reduction vastly reduces the runtime of a parameter fit and essentially allows to eliminate the need to adjust the initialization to the input data using multiple fits on a uniform search space. With this approach, we are able to estimate the GFR in three of four clinical cases within 10% of the clinically measured GFR.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hodneland E, et al. In vivo estimation of glomerular filtration in the kidney using DCE-MRI. Proc Int Symp Image Signal Process Anal. 2011; p. 755–61.
Annet ML, et al. Glomerular filtration rate: assessment with dynamic contrastenhanced MRI and cortical-compartment model in the rabbit kidney. J Magn Reson Imaging. 2004; p. 843–9.
Huang A, et al. MR imaging of renal function. Radiol Clin North Am. 2003;41:1001–17.
Bauer L, et al. Clinical appraisal of creatinine clearance as a measurement of glomerular filtration rate. Am J Kidney Dis. 1982;2:337–46.
Myers GL, et al. Recommendations for improving serum creatinine measurment: a report from the laboratory working group of the national kidney disease education program. Clin Chem. 2006; p. 5–18.
Sourbron PSP, et al. MRI-measurment of perfusion and glomerular filtration in the human kidney with a separable compartment model. Invest Radiol. 2008;43(1):40–8.
MATLAB. Version 7.10.0 (R2010a). Natick, MA: The MathWorks Inc.; 2010.
Parker GJ, et al. Automated arterial input function extraction for T1-weighted DCE-MRI. Proc Int Soc Magn Reson Med Sci Meet Exhib. 2003;1.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Trull, A., Berkels, B., Modersitzki, J. (2014). Glomerular Filtration Rate Estimation from Dynamic Contrast-Enhanced MRI. In: Deserno, T., Handels, H., Meinzer, HP., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2014. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54111-7_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-54111-7_11
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54110-0
Online ISBN: 978-3-642-54111-7
eBook Packages: Computer Science and Engineering (German Language)