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Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning
Introduction. With biomedical imaging research increasingly using large datasets, it becomes critical to find operator-free methods to quality control the data collected and the associated analysis. Attempts to use artificial intelligence (AI) to perform automated quality control (QC) for both singl...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Scientific Publishers
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8404039/ https://www.ncbi.nlm.nih.gov/pubmed/34289438 http://dx.doi.org/10.1016/j.cmpb.2021.106239 |
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author | Pontoriero, Antonella D. Nordio, Giovanna Easmin, Rubaida Giacomel, Alessio Santangelo, Barbara Jahuar, Sameer Bonoldi, Ilaria Rogdaki, Maria Turkheimer, Federico Howes, Oliver Veronese, Mattia |
author_facet | Pontoriero, Antonella D. Nordio, Giovanna Easmin, Rubaida Giacomel, Alessio Santangelo, Barbara Jahuar, Sameer Bonoldi, Ilaria Rogdaki, Maria Turkheimer, Federico Howes, Oliver Veronese, Mattia |
author_sort | Pontoriero, Antonella D. |
collection | PubMed |
description | Introduction. With biomedical imaging research increasingly using large datasets, it becomes critical to find operator-free methods to quality control the data collected and the associated analysis. Attempts to use artificial intelligence (AI) to perform automated quality control (QC) for both single-site and multi-site datasets have been explored in some neuroimaging techniques (e.g. EEG or MRI), although these methods struggle to find replication in other domains. The aim of this study is to test the feasibility of an automated QC pipeline for brain [(18)F]-FDOPA PET imaging as a biomarker for the dopamine system. Methods. Two different Convolutional Neural Networks (CNNs) were used and combined to assess spatial misalignment to a standard template and the signal-to-noise ratio (SNR) relative to 200 static [(18)F]-FDOPA PET images that had been manually quality controlled from three different PET/CT scanners. The scans were combined with an additional 400 scans, in which misalignment (200 scans) and low SNR (200 scans) were simulated. A cross-validation was performed, where 80% of the data were used for training and 20% for validation. Two additional datasets of [(18)F]-FDOPA PET images (50 and 100 scans respectively with at least 80% of good quality images) were used for out-of-sample validation. Results. The CNN performance was excellent in the training dataset (accuracy for motion: 0.86 ± 0.01, accuracy for SNR: 0.69 ± 0.01), leading to 100% accurate QC classification when applied to the two out-of-sample datasets. Data dimensionality reduction affected the generalizability of the CNNs, especially when the classifiers were applied to the out-of-sample data from 3D to 1D datasets. Conclusions. This feasibility study shows that it is possible to perform automatic QC of [(18)F]-FDOPA PET imaging with CNNs. The approach has the potential to be extended to other PET tracers in both brain and non-brain applications, but it is dependent on the availability of large datasets necessary for the algorithm training. |
format | Online Article Text |
id | pubmed-8404039 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier Scientific Publishers |
record_format | MEDLINE/PubMed |
spelling | pubmed-84040392021-09-02 Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning Pontoriero, Antonella D. Nordio, Giovanna Easmin, Rubaida Giacomel, Alessio Santangelo, Barbara Jahuar, Sameer Bonoldi, Ilaria Rogdaki, Maria Turkheimer, Federico Howes, Oliver Veronese, Mattia Comput Methods Programs Biomed Article Introduction. With biomedical imaging research increasingly using large datasets, it becomes critical to find operator-free methods to quality control the data collected and the associated analysis. Attempts to use artificial intelligence (AI) to perform automated quality control (QC) for both single-site and multi-site datasets have been explored in some neuroimaging techniques (e.g. EEG or MRI), although these methods struggle to find replication in other domains. The aim of this study is to test the feasibility of an automated QC pipeline for brain [(18)F]-FDOPA PET imaging as a biomarker for the dopamine system. Methods. Two different Convolutional Neural Networks (CNNs) were used and combined to assess spatial misalignment to a standard template and the signal-to-noise ratio (SNR) relative to 200 static [(18)F]-FDOPA PET images that had been manually quality controlled from three different PET/CT scanners. The scans were combined with an additional 400 scans, in which misalignment (200 scans) and low SNR (200 scans) were simulated. A cross-validation was performed, where 80% of the data were used for training and 20% for validation. Two additional datasets of [(18)F]-FDOPA PET images (50 and 100 scans respectively with at least 80% of good quality images) were used for out-of-sample validation. Results. The CNN performance was excellent in the training dataset (accuracy for motion: 0.86 ± 0.01, accuracy for SNR: 0.69 ± 0.01), leading to 100% accurate QC classification when applied to the two out-of-sample datasets. Data dimensionality reduction affected the generalizability of the CNNs, especially when the classifiers were applied to the out-of-sample data from 3D to 1D datasets. Conclusions. This feasibility study shows that it is possible to perform automatic QC of [(18)F]-FDOPA PET imaging with CNNs. The approach has the potential to be extended to other PET tracers in both brain and non-brain applications, but it is dependent on the availability of large datasets necessary for the algorithm training. Elsevier Scientific Publishers 2021-09 /pmc/articles/PMC8404039/ /pubmed/34289438 http://dx.doi.org/10.1016/j.cmpb.2021.106239 Text en © 2021 The Authors. Published by Elsevier B.V. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Pontoriero, Antonella D. Nordio, Giovanna Easmin, Rubaida Giacomel, Alessio Santangelo, Barbara Jahuar, Sameer Bonoldi, Ilaria Rogdaki, Maria Turkheimer, Federico Howes, Oliver Veronese, Mattia Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning |
title | Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning |
title_full | Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning |
title_fullStr | Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning |
title_full_unstemmed | Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning |
title_short | Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning |
title_sort | automated data quality control in fdopa brain pet imaging using deep learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8404039/ https://www.ncbi.nlm.nih.gov/pubmed/34289438 http://dx.doi.org/10.1016/j.cmpb.2021.106239 |
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