Abstract
Prostate cancer is one of the commonest cancers in the world. Dynamic contrast enhanced MRI (DCE-MRI) provides
an opportunity for non-invasive diagnosis, staging, and treatment monitoring. Quantitative analysis of DCE-MRI relies on
determination of an accurate arterial input function (AIF). Although several methods for automated AIF detection have
been proposed in literature, none are optimized for use in prostate DCE-MRI, which is particularly challenging due to
large spatial signal inhomogeneity. In this paper, we propose a fully automated method for determining the AIF from
prostate DCE-MRI. Our method is based on modeling pixel uptake curves as gamma variate functions (GVF). First, we
analytically compute bounds on GVF parameters for more robust fitting. Next, we approximate a GVF for each pixel
based on local time domain information, and eliminate the pixels with false estimated AIFs using the deduced upper and
lower bounds. This makes the algorithm robust to signal inhomogeneity. After that, according to spatial information such
as similarity and distance between pixels, we formulate the global AIF selection as an energy minimization problem and
solve it using a message passing algorithm to further rule out the weak pixels and optimize the detected AIF. Our method
is fully automated without training or a priori setting of parameters. Experimental results on clinical data have shown that
our method obtained promising detection accuracy (all detected pixels inside major arteries), and a very good match with
expert traced manual AIF.
