  Dynamic 2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography
(dFDG-PET) for human brain imaging has considerable clinical potential, yet its
utilization remains limited. A key challenge in the quantitative analysis of
dFDG-PET is characterizing a patient-specific blood input function,
traditionally reliant on invasive arterial blood sampling. This research
introduces a novel approach employing non-invasive deep learning model-based
computations from the internal carotid arteries (ICA) with partial volume (PV)
corrections, thereby eliminating the need for invasive arterial sampling. We
present an end-to-end pipeline incorporating a 3D U-Net based ICA-net for ICA
segmentation, alongside a Recurrent Neural Network (RNN) based MCIF-net for the
derivation of a model-corrected blood input function (MCIF) with PV
corrections. The developed 3D U-Net and RNN was trained and validated using a
5-fold cross-validation approach on 50 human brain FDG PET datasets. The
ICA-net achieved an average Dice score of 82.18% and an Intersection over Union
of 68.54% across all tested scans. Furthermore, the MCIF-net exhibited a
minimal root mean squared error of 0.0052. The application of this pipeline to
ground truth data for dFDG-PET brain scans resulted in the precise localization
of seizure onset regions, which contributed to a successful clinical outcome,
with the patient achieving a seizure-free state after treatment. These results
underscore the efficacy of the ICA-net and MCIF-net deep learning pipeline in
learning the ICA structure's distribution and automating MCIF computation with
PV corrections. This advancement marks a significant leap in non-invasive
neuroimaging.
