Quantitative analysis of contrast-enhanced dynamic MR images has potential for diagnosing prostate cancer. Contemporary fast acquisition techniques can give sufficiently high temporal resolution to sample the fast dynamics observed in the prostate. Data reduction for parametric visualization requires automatic curve fitting to a pharmacokinetic model, which to date has been performed using least-squares error minimization methods. We observed that these methods often produce unexpectedly noisy estimates, especially for the typically fast, intermediate parameters time-to-peak and start-of-enhancement, resulting in inaccurate pharmacokinetic parameter estimates. We developed a new curve fit method that focuses on the most probable slope. A set of 10 patients annotated using histopathology was used to compare the conventional and new methods. The results show that our new method is significantly more accurate, especially in the relatively less-enhancing peripheral zone. We conclude that estimation accuracy depends on the curve fit method, which is especially important when evaluating the peripheral zone of the prostate.