OBJECTIVES: We sought to compare pharmacokinetic modeling of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data by assuming a linear and nonlinear relationship between signal intensity (SI) and contrast agent (CA) concentration. MATERIALS AND METHODS: Data sets were generated by computer-based simulation studies and DCE-MRI examination of 5 tumor-bearing mice using a 1.5 T MR-scanner. Two approaches were investigated: a linear and nonlinear relationship between SI and CA concentration before pharmacokinetic analysis. In a pharmacokinetic 2-compartment model, values of exchange rate constant kep and amplitude A were compared for both assumptions. RESULTS: In the linear approach, A was as much as 30% less for kep values between 1.0 and 5.0 min, whereas kep was as much as 60% greater, for kep between 0.2 and 5.0 min compared with the nonlinear one, as demonstrated in simulations and animal studies. CONCLUSIONS: Nonlinearity between SI and CA concentration has to be considered for accurate parameter calculation in DCE-MRI studies.
OBJECTIVES: We sought to compare pharmacokinetic modeling of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data by assuming a linear and nonlinear relationship between signal intensity (SI) and contrast agent (CA) concentration. MATERIALS AND METHODS: Data sets were generated by computer-based simulation studies and DCE-MRI examination of 5 tumor-bearing mice using a 1.5 T MR-scanner. Two approaches were investigated: a linear and nonlinear relationship between SI and CA concentration before pharmacokinetic analysis. In a pharmacokinetic 2-compartment model, values of exchange rate constant kep and amplitude A were compared for both assumptions. RESULTS: In the linear approach, A was as much as 30% less for kep values between 1.0 and 5.0 min, whereas kep was as much as 60% greater, for kep between 0.2 and 5.0 min compared with the nonlinear one, as demonstrated in simulations and animal studies. CONCLUSIONS: Nonlinearity between SI and CA concentration has to be considered for accurate parameter calculation in DCE-MRI studies.
Authors: Louisa Bokacheva; Khushali Kotedia; Megan Reese; Sally-Ann Ricketts; Jane Halliday; Carl H Le; Jason A Koutcher; Sean Carlin Journal: NMR Biomed Date: 2012-07-08 Impact factor: 4.044
Authors: Ergys Subashi; Francisco J Cordero; Kyle G Halvorson; Yi Qi; John C Nouls; Oren J Becher; G Allan Johnson Journal: J Neurooncol Date: 2015-10-28 Impact factor: 4.130
Authors: Ergys Subashi; Everett J Moding; Gary P Cofer; James R MacFall; David G Kirsch; Yi Qi; G Allan Johnson Journal: Med Phys Date: 2013-02 Impact factor: 4.071