  Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used to
quantify perfusion and vascular permeability. In most cases a bolus arrival
time (BAT) delay exists between the arterial input function (AIF) and the
contrast agent arrival in the tissue of interest which needs to be estimated.
Existing methods for BAT estimation are tailored to tissue concentration curves
which have a fast upslope to the peak as frequently observed in patient data.
However, they may give poor results for curves that do not have this
characteristic shape such as tissue concentration curves of small animals. In
this paper, we propose a novel method for BAT estimation of signals that do not
have a fast upslope to their peak. The model is based on splines which are able
to adapt to a large variety of concentration curves. Furthermore, the method
estimates BATs on a continuous time scale. All relevant model parameters are
automatically determined by generalized cross validation. We use simulated
concentration curves of small animal and patient settings to assess the
accuracy and robustness of our approach. The proposed method outperforms a
state-of-the-art method for small animal data and it gives competitive results
for patient data. Finally, it is tested on in vivo acquired rat data where
accuracy of BAT estimation was also improved upon the state-of-the-art method.
The results indicate that the proposed method is suitable for accurate BAT
estimation of DCE-MRI data, especially for small animals.
