Influence of Parameterization on Tracer Kinetic Modeling in DCE-MRI

Tracer kinetic modeling in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is commonly performed using least squares algorithms. The convergence of such algorithms and the repeatability of the estimates are affected by the curvature of the model’s expectation surface. An adequate choice of the parameterization can reduce curvature and thus improve parameter estimation. This study analyzes the influence of two parameterizations on the curvature of the Tofts model. The influences of the total acquisition time and the sampling period are evaluated. Analysis results show that using (Ktrans, ve ) can significantly reduce the curvature in a large area of the parameter space, suggesting that curvature analysis could guide the choice of the best local parameterization in Gauss-Newton-based algorithms. In addition, increasing the total acquisition time and decreasing the sampling period reduce the curvature. However, only slight improvements are obtained for a total time longer than about 6 min and a sampling period shorter than approximately 10 s