A Novel Estimation Method for Physiological Parameters in Dynamic Contrast-Enhanced MRI: Application of a Distributed Parameter Model Using Fourier-Domain Calculations

• Published in: IEEE transactions on medical imaging Vol. 28; no. 9; pp. 1375 - 1383
• Main Authors: Garpebring, A., Ostlund, N., Karlsson, M.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2009; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximation algorithms; Blood flow; Brain Neoplasms - pathology; Brain Neoplasms - secondary; Computer Simulation; Contrast Media; Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI); Extracellular; Female; Fourier Analysis; Humans; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Mathematical model; Middle Aged; Models, Biological; Monte Carlo Method; Neoplasms; Parameter estimation; Permeability; Physiology; Studies; Testing; tissue homogeneity models; tracer kinetics
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2009.2016212

Dynamic contrast-enhanced magnetic resonance imaging (MRI) is a promising tool in the evaluation of tumor physiology. From rapidly acquired images and a model for contrast agent pharmacokinetics, physiological parameters are derived. One pharmacokinetic model, the tissue homogeneity model, enables estimation of both blood flow and vessel permeability together with parameters that describe blood volume and extracellular extravascular volume fraction. However, studies have shown that parameter estimation with this model is unstable. Therefore, several initial guesses are needed for accurate estimates, which makes the estimation slow. In this study a new estimation algorithm for the tissue homogeneity model, based on Fourier domain calculations, was derived and implemented as a Matlab program. The algorithm was tested with Monte-Carlo simulations and the results were compared to an existing method that uses the adiabatic approximation. The algorithm was also tested on data from a metastasis in the brain. The comparison showed that the new algorithm gave more accurate results on the 2.5th and 97.5th percentile levels, for instance the error in blood volume was reduced by 21%. In addition, the time needed for the computations was reduced with a factor 25. It was concluded that the new algorithm can be used to speed up parameter estimation while accuracy can be gained at the same time.