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Enhancing magnetic resonance imaging-driven Alzheimer’s disease classification performance using generative adversarial learning

BACKGROUND: Generative adversarial networks (GAN) can produce images of improved quality but their ability to augment image-based classification is not fully explored. We evaluated if a modified GAN can learn from magnetic resonance imaging (MRI) scans of multiple magnetic field strengths to enhance...

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Autores principales: Zhou, Xiao, Qiu, Shangran, Joshi, Prajakta S., Xue, Chonghua, Killiany, Ronald J., Mian, Asim Z., Chin, Sang P., Au, Rhoda, Kolachalama, Vijaya B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7958452/
https://www.ncbi.nlm.nih.gov/pubmed/33715635
http://dx.doi.org/10.1186/s13195-021-00797-5
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author Zhou, Xiao
Qiu, Shangran
Joshi, Prajakta S.
Xue, Chonghua
Killiany, Ronald J.
Mian, Asim Z.
Chin, Sang P.
Au, Rhoda
Kolachalama, Vijaya B.
author_facet Zhou, Xiao
Qiu, Shangran
Joshi, Prajakta S.
Xue, Chonghua
Killiany, Ronald J.
Mian, Asim Z.
Chin, Sang P.
Au, Rhoda
Kolachalama, Vijaya B.
author_sort Zhou, Xiao
collection PubMed
description BACKGROUND: Generative adversarial networks (GAN) can produce images of improved quality but their ability to augment image-based classification is not fully explored. We evaluated if a modified GAN can learn from magnetic resonance imaging (MRI) scans of multiple magnetic field strengths to enhance Alzheimer’s disease (AD) classification performance. METHODS: T1-weighted brain MRI scans from 151 participants of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), who underwent both 1.5-Tesla (1.5-T) and 3-Tesla imaging at the same time were selected to construct a GAN model. This model was trained along with a three-dimensional fully convolutional network (FCN) using the generated images (3T*) as inputs to predict AD status. Quality of the generated images was evaluated using signal to noise ratio (SNR), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Natural Image Quality Evaluator (NIQE). Cases from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL, n = 107) and the National Alzheimer’s Coordinating Center (NACC, n = 565) were used for model validation. RESULTS: The 3T*-based FCN classifier performed better than the FCN model trained using the 1.5-T scans. Specifically, the mean area under curve increased from 0.907 to 0.932, from 0.934 to 0.940, and from 0.870 to 0.907 on the ADNI test, AIBL, and NACC datasets, respectively. Additionally, we found that the mean quality of the generated (3T*) images was consistently higher than the 1.5-T images, as measured using SNR, BRISQUE, and NIQE on the validation datasets. CONCLUSION: This study demonstrates a proof of principle that GAN frameworks can be constructed to augment AD classification performance and improve image quality. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13195-021-00797-5.
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spelling pubmed-79584522021-03-16 Enhancing magnetic resonance imaging-driven Alzheimer’s disease classification performance using generative adversarial learning Zhou, Xiao Qiu, Shangran Joshi, Prajakta S. Xue, Chonghua Killiany, Ronald J. Mian, Asim Z. Chin, Sang P. Au, Rhoda Kolachalama, Vijaya B. Alzheimers Res Ther Research BACKGROUND: Generative adversarial networks (GAN) can produce images of improved quality but their ability to augment image-based classification is not fully explored. We evaluated if a modified GAN can learn from magnetic resonance imaging (MRI) scans of multiple magnetic field strengths to enhance Alzheimer’s disease (AD) classification performance. METHODS: T1-weighted brain MRI scans from 151 participants of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), who underwent both 1.5-Tesla (1.5-T) and 3-Tesla imaging at the same time were selected to construct a GAN model. This model was trained along with a three-dimensional fully convolutional network (FCN) using the generated images (3T*) as inputs to predict AD status. Quality of the generated images was evaluated using signal to noise ratio (SNR), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Natural Image Quality Evaluator (NIQE). Cases from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL, n = 107) and the National Alzheimer’s Coordinating Center (NACC, n = 565) were used for model validation. RESULTS: The 3T*-based FCN classifier performed better than the FCN model trained using the 1.5-T scans. Specifically, the mean area under curve increased from 0.907 to 0.932, from 0.934 to 0.940, and from 0.870 to 0.907 on the ADNI test, AIBL, and NACC datasets, respectively. Additionally, we found that the mean quality of the generated (3T*) images was consistently higher than the 1.5-T images, as measured using SNR, BRISQUE, and NIQE on the validation datasets. CONCLUSION: This study demonstrates a proof of principle that GAN frameworks can be constructed to augment AD classification performance and improve image quality. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13195-021-00797-5. BioMed Central 2021-03-14 /pmc/articles/PMC7958452/ /pubmed/33715635 http://dx.doi.org/10.1186/s13195-021-00797-5 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.
spellingShingle Research
Zhou, Xiao
Qiu, Shangran
Joshi, Prajakta S.
Xue, Chonghua
Killiany, Ronald J.
Mian, Asim Z.
Chin, Sang P.
Au, Rhoda
Kolachalama, Vijaya B.
Enhancing magnetic resonance imaging-driven Alzheimer’s disease classification performance using generative adversarial learning
title Enhancing magnetic resonance imaging-driven Alzheimer’s disease classification performance using generative adversarial learning
title_full Enhancing magnetic resonance imaging-driven Alzheimer’s disease classification performance using generative adversarial learning
title_fullStr Enhancing magnetic resonance imaging-driven Alzheimer’s disease classification performance using generative adversarial learning
title_full_unstemmed Enhancing magnetic resonance imaging-driven Alzheimer’s disease classification performance using generative adversarial learning
title_short Enhancing magnetic resonance imaging-driven Alzheimer’s disease classification performance using generative adversarial learning
title_sort enhancing magnetic resonance imaging-driven alzheimer’s disease classification performance using generative adversarial learning
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7958452/
https://www.ncbi.nlm.nih.gov/pubmed/33715635
http://dx.doi.org/10.1186/s13195-021-00797-5
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