Fitting the two-compartment model in DCE-MRI by linear inversion

Purpose: Model fitting of DCE-MRI data with non-linear least squares (NLLS) methods is slow and may be biased by the choice of initial values. The aim of this study was to develop and evaluate a linear least-squares (LLS) method to fit the two-compartment exchange and -filtration models. Methods: A second-order linear differential equation for the measured concentrations was derived where model parameters act as coefficients. Simulations of normal and pathological data were performed to determine calculation time, accuracy and precision under different noise levels and temporal resolutions. Performance of the LLS was evaluated by comparison against the NLLS. Results: The LLS method is about 200 times faster, which reduces the calculation times for a 256_256 MR slice from 9 min to 3 sec. For ideal data with low noise and high temporal resolution the LLS and NLLS were equally accurate and precise. The LLS was more accurate and precise than the NLLS at low temporal resolution, but less accurate at high noise levels. Conclusion: The data show that the LLS leads to a significant reduction in calculation times, and more reliable results at low noise levels. At higher noise levels the LLS becomes exceedingly inaccurate compared to the NLLS, but this may be improved by using a suitable weighting strategy.