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Standardized quality metric system for structural brain magnetic resonance images in multi-center neuroimaging study

BACKGROUND: Multi-site neuroimaging offer several benefits and poses tough challenges in the drug development process. Although MRI protocol and clinical guidelines developed to address these challenges recommend the use of good quality images, reliable assessment of image quality is hampered by the...

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Autores principales: Osadebey, Michael E., Pedersen, Marius, Arnold, Douglas L., Wendel-Mitoraj, Katrina E., Alzheimer’s Disease Neuroimaging Initiative, for the
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6142697/
https://www.ncbi.nlm.nih.gov/pubmed/30223797
http://dx.doi.org/10.1186/s12880-018-0266-4
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author Osadebey, Michael E.
Pedersen, Marius
Arnold, Douglas L.
Wendel-Mitoraj, Katrina E.
Alzheimer’s Disease Neuroimaging Initiative, for the
author_facet Osadebey, Michael E.
Pedersen, Marius
Arnold, Douglas L.
Wendel-Mitoraj, Katrina E.
Alzheimer’s Disease Neuroimaging Initiative, for the
author_sort Osadebey, Michael E.
collection PubMed
description BACKGROUND: Multi-site neuroimaging offer several benefits and poses tough challenges in the drug development process. Although MRI protocol and clinical guidelines developed to address these challenges recommend the use of good quality images, reliable assessment of image quality is hampered by the several shortcomings of existing techniques. METHODS: Given a test image two feature images are extracted. They are grayscale and contrast feature images. Four binary images are generated by setting four different global thresholds on the feature images. Image quality is predicted by measuring the structural similarity between appropriate pairs of binary images. The lower and upper limits of the quality index are 0 and 1. Quality prediction is based on four quality attributes; luminance contrast, texture, texture contrast and lightness. RESULTS: Performance evaluation on test data from three multi-site clinical trials show good objective quality evaluation across MRI sequences, levels of distortion and quality attributes. Correlation with subjective evaluation by human observers is ≥ 0.6. CONCLUSION: The results are promising for the evaluation of MRI protocols, specifically the standardization of quality index, designed to overcome the challenges encountered in multi-site clinical trials.
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spelling pubmed-61426972018-09-21 Standardized quality metric system for structural brain magnetic resonance images in multi-center neuroimaging study Osadebey, Michael E. Pedersen, Marius Arnold, Douglas L. Wendel-Mitoraj, Katrina E. Alzheimer’s Disease Neuroimaging Initiative, for the BMC Med Imaging Research Article BACKGROUND: Multi-site neuroimaging offer several benefits and poses tough challenges in the drug development process. Although MRI protocol and clinical guidelines developed to address these challenges recommend the use of good quality images, reliable assessment of image quality is hampered by the several shortcomings of existing techniques. METHODS: Given a test image two feature images are extracted. They are grayscale and contrast feature images. Four binary images are generated by setting four different global thresholds on the feature images. Image quality is predicted by measuring the structural similarity between appropriate pairs of binary images. The lower and upper limits of the quality index are 0 and 1. Quality prediction is based on four quality attributes; luminance contrast, texture, texture contrast and lightness. RESULTS: Performance evaluation on test data from three multi-site clinical trials show good objective quality evaluation across MRI sequences, levels of distortion and quality attributes. Correlation with subjective evaluation by human observers is ≥ 0.6. CONCLUSION: The results are promising for the evaluation of MRI protocols, specifically the standardization of quality index, designed to overcome the challenges encountered in multi-site clinical trials. BioMed Central 2018-09-17 /pmc/articles/PMC6142697/ /pubmed/30223797 http://dx.doi.org/10.1186/s12880-018-0266-4 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Osadebey, Michael E.
Pedersen, Marius
Arnold, Douglas L.
Wendel-Mitoraj, Katrina E.
Alzheimer’s Disease Neuroimaging Initiative, for the
Standardized quality metric system for structural brain magnetic resonance images in multi-center neuroimaging study
title Standardized quality metric system for structural brain magnetic resonance images in multi-center neuroimaging study
title_full Standardized quality metric system for structural brain magnetic resonance images in multi-center neuroimaging study
title_fullStr Standardized quality metric system for structural brain magnetic resonance images in multi-center neuroimaging study
title_full_unstemmed Standardized quality metric system for structural brain magnetic resonance images in multi-center neuroimaging study
title_short Standardized quality metric system for structural brain magnetic resonance images in multi-center neuroimaging study
title_sort standardized quality metric system for structural brain magnetic resonance images in multi-center neuroimaging study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6142697/
https://www.ncbi.nlm.nih.gov/pubmed/30223797
http://dx.doi.org/10.1186/s12880-018-0266-4
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