Dynamic contrast-enhanced MRI is a dynamic imaging technique that is now widely used for cancer imaging. Changes in tumour microvasculature are typically quantified by pharmacokinetic modelling of the contrast
uptake curves. Reliable pharmacokinetic parameter estimation depends on the measurement of the arterial input function, which can be obtained from arterial blood sampling, or extracted from the image data directly.
However, arterial blood sampling poses additional risks to the patient, and extracting the input function from
MR intensities is not reliable. In this work, we propose to compute a perfusion CT based arterial input function,
which is then employed for dynamic contrast enhanced MRI pharmacokinetic parameter estimation. Here, parameter estimation is performed simultaneously with intra-sequence motion correction by using nonlinear image
registration. Ktrans maps obtained with this approach were compared with those obtained using a population
averaged arterial input function, i.e. Orton. The dataset comprised 5 rectal cancer patients, who had been
imaged with both perfusion CT and dynamic contrast enhanced MRI, before and after the administration of a
radiosensitising drug. Ktrans distributions pre and post therapy were computed using both the perfusion CT and
the Orton arterial input function. Perfusion CT derived arterial input functions can be used for pharmacokinetic
modelling of dynamic contrast enhanced MRI data, when perfusion CT images of the same patients are available.
Compared to the Orton model, perfusion CT functions have the potential to give a more accurate separation
between responders and non-responders.