An end-to-end deep learning pipeline to derive blood input with partial
  volume corrections for automated parametric brain PET mapping
Rugved Chavan Gabriel Hyman Zoraiz Qureshi Nivetha Jayakumar William Terrell Stuart Berr David Schiff Megan Wardius Nathan Fountain Thomas Muttikkal Mark Quigg Miaomiao Zhang Bijoy Kundu
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.