An end-to-end deep learning pipeline to derive blood input with partial volume corrections for automated parametric brain PET mapping
<|reference_start|>An end-to-end deep learning pipeline to derive blood input with partial volume corrections for automated parametric brain PET mapping: 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.<|reference_end|>
@article{chavan2024an,
    title={An end-to-end deep learning pipeline to derive blood input with partial
  volume corrections for automated parametric brain PET mapping},
    author={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},
    journal={arXiv preprint arXiv:2402.03414},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.03414},
    primaryClass={eess.IV cs.CV cs.LG}
}