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APPIAN: Automated Pipeline for PET Image Analysis
APPIAN is an automated pipeline for user-friendly and reproducible analysis of positron emission tomography (PET) images with the aim of automating all processing steps up to the statistical analysis of measures derived from the final output images. The three primary processing steps are coregistrat...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6178989/ https://www.ncbi.nlm.nih.gov/pubmed/30337866 http://dx.doi.org/10.3389/fninf.2018.00064 |
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author | Funck, Thomas Larcher, Kevin Toussaint, Paule-Joanne Evans, Alan C. Thiel, Alexander |
author_facet | Funck, Thomas Larcher, Kevin Toussaint, Paule-Joanne Evans, Alan C. Thiel, Alexander |
author_sort | Funck, Thomas |
collection | PubMed |
description | APPIAN is an automated pipeline for user-friendly and reproducible analysis of positron emission tomography (PET) images with the aim of automating all processing steps up to the statistical analysis of measures derived from the final output images. The three primary processing steps are coregistration of PET images to T1-weighted magnetic resonance (MR) images, partial-volume correction (PVC), and quantification with tracer kinetic modeling. While there are alternate open-source PET pipelines, none offers all of the features necessary for making automated PET analysis as reliably, flexibly and easily extendible as possible. To this end, a novel method for automated quality control (QC) has been designed to facilitate reliable, reproducible research by helping users verify that each processing stage has been performed as expected. Additionally, a web browser-based GUI has been implemented to allow both the 3D visualization of the output images, as well as plots describing the quantitative results of the analyses performed by the pipeline. APPIAN also uses flexible region of interest (ROI) definition—with both volumetric and, optionally, surface-based ROI—to allow users to analyze data from a wide variety of experimental paradigms, e.g., longitudinal lesion studies, large cross-sectional population studies, multi-factorial experimental designs, etc. Finally, APPIAN is designed to be modular so that users can easily test new algorithms for PVC or quantification or add entirely new analyses to the basic pipeline. We validate the accuracy of APPIAN against the Monte-Carlo simulated SORTEO database and show that, after PVC, APPIAN recovers radiotracer concentrations within 93–100% accuracy. |
format | Online Article Text |
id | pubmed-6178989 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-61789892018-10-18 APPIAN: Automated Pipeline for PET Image Analysis Funck, Thomas Larcher, Kevin Toussaint, Paule-Joanne Evans, Alan C. Thiel, Alexander Front Neuroinform Neuroinformatics APPIAN is an automated pipeline for user-friendly and reproducible analysis of positron emission tomography (PET) images with the aim of automating all processing steps up to the statistical analysis of measures derived from the final output images. The three primary processing steps are coregistration of PET images to T1-weighted magnetic resonance (MR) images, partial-volume correction (PVC), and quantification with tracer kinetic modeling. While there are alternate open-source PET pipelines, none offers all of the features necessary for making automated PET analysis as reliably, flexibly and easily extendible as possible. To this end, a novel method for automated quality control (QC) has been designed to facilitate reliable, reproducible research by helping users verify that each processing stage has been performed as expected. Additionally, a web browser-based GUI has been implemented to allow both the 3D visualization of the output images, as well as plots describing the quantitative results of the analyses performed by the pipeline. APPIAN also uses flexible region of interest (ROI) definition—with both volumetric and, optionally, surface-based ROI—to allow users to analyze data from a wide variety of experimental paradigms, e.g., longitudinal lesion studies, large cross-sectional population studies, multi-factorial experimental designs, etc. Finally, APPIAN is designed to be modular so that users can easily test new algorithms for PVC or quantification or add entirely new analyses to the basic pipeline. We validate the accuracy of APPIAN against the Monte-Carlo simulated SORTEO database and show that, after PVC, APPIAN recovers radiotracer concentrations within 93–100% accuracy. Frontiers Media S.A. 2018-09-26 /pmc/articles/PMC6178989/ /pubmed/30337866 http://dx.doi.org/10.3389/fninf.2018.00064 Text en Copyright © 2018 Funck, Larcher, Toussaint, Evans and Thiel. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroinformatics Funck, Thomas Larcher, Kevin Toussaint, Paule-Joanne Evans, Alan C. Thiel, Alexander APPIAN: Automated Pipeline for PET Image Analysis |
title | APPIAN: Automated Pipeline for PET Image Analysis |
title_full | APPIAN: Automated Pipeline for PET Image Analysis |
title_fullStr | APPIAN: Automated Pipeline for PET Image Analysis |
title_full_unstemmed | APPIAN: Automated Pipeline for PET Image Analysis |
title_short | APPIAN: Automated Pipeline for PET Image Analysis |
title_sort | appian: automated pipeline for pet image analysis |
topic | Neuroinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6178989/ https://www.ncbi.nlm.nih.gov/pubmed/30337866 http://dx.doi.org/10.3389/fninf.2018.00064 |
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