An end-to-end deep learning pipeline to derive blood input with partial volume corrections for automated parametric brain PET mapping
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| Auteurs principaux: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916116257832960 |
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| author | Chavan, Rugved Hyman, Gabriel Qureshi, Zoraiz Jayakumar, Nivetha Terrell, William Berr, Stuart Schiff, David Wardius, Megan Fountain, Nathan Muttikkal, Thomas Quigg, Mark Zhang, Miaomiao Kundu, Bijoy |
| author_facet | Chavan, Rugved Hyman, Gabriel Qureshi, Zoraiz Jayakumar, Nivetha Terrell, William Berr, Stuart Schiff, David Wardius, Megan Fountain, Nathan Muttikkal, Thomas Quigg, Mark Zhang, Miaomiao Kundu, Bijoy |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_03414 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An end-to-end deep learning pipeline to derive blood input with partial volume corrections for automated parametric brain PET mapping Chavan, Rugved Hyman, Gabriel Qureshi, Zoraiz Jayakumar, Nivetha Terrell, William Berr, Stuart Schiff, David Wardius, Megan Fountain, Nathan Muttikkal, Thomas Quigg, Mark Zhang, Miaomiao Kundu, Bijoy Image and Video Processing Computer Vision and Pattern Recognition Machine Learning 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. |
| title | An end-to-end deep learning pipeline to derive blood input with partial volume corrections for automated parametric brain PET mapping |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2402.03414 |