Arterial Input Function and Tracer Kinetic Model Driven Network for Rapid Inference of Kinetic Maps in Dynamic Contrast Enhanced MRI (AIF-TK-Net)

We propose a patient-specific arterial input function (AIF) and tracer kinetic (TK) model-driven network to rapidly estimate the extended Tofts-Kety kinetic model parameters in DCE-MRI. We term our network as AIF-TK-net, which maps an input comprising of an image patch of the DCE-time series and the patient-specific AIF to the output image patch of the TK parameters. We leverage the open-source NEURO-RIDER database of brain tumor DCE-MRI scans to train our network. Once trained, our model rapidly infers the TK maps of unseen DCE-MRI images on the order of a 0.34 sec/slice for a 256x256x65 time series data on a NVIDIA GeForce GTX 1080 Ti GPU. We show its utility on high time resolution DCE-MRI datasets where significant variability in AIFs across patients exists. We demonstrate that the proposed AIF-TK net considerably improves the TK parameter estimation accuracy in comparison to a network, which does not utilize the patient AIF.