Fast three‐dimensional image generation for healthy brain aging using diffeomorphic registration
Predicting brain aging can help in the early detection and prognosis of neurodegenerative diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance imaging (MRI) have been essential to understand the structural brain changes due to aging. However, these cohorts suffer fro...
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| Published in | Human brain mapping Vol. 44; no. 4; pp. 1289 - 1308 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken, USA
John Wiley & Sons, Inc
01.03.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1065-9471 1097-0193 1097-0193 |
| DOI | 10.1002/hbm.26165 |
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| Abstract | Predicting brain aging can help in the early detection and prognosis of neurodegenerative diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance imaging (MRI) have been essential to understand the structural brain changes due to aging. However, these cohorts suffer from missing data due to logistic issues in the recruitment of subjects. This paper proposes a methodology for filling up missing data in longitudinal cohorts with anatomically plausible images that capture the subject‐specific aging process. The proposed methodology is developed within the framework of diffeomorphic registration. First, two novel modules are introduced within Synthmorph, a fast, state‐of‐the‐art deep learning‐based diffeomorphic registration method, to simulate the aging process between the first and last available MRI scan for each subject in three‐dimensional (3D). The use of image registration also makes the generated images plausible by construction. Second, we used six image similarity measurements to rearrange the generated images to the specific age range. Finally, we estimated the age of every generated image by using the assumption of linear brain decay in healthy subjects. The methodology was evaluated on 2662 T1‐weighted MRI scans from 796 healthy participants from 3 different longitudinal cohorts: Alzheimer's Disease Neuroimaging Initiative, Open Access Series of Imaging Studies‐3, and Group of Neuropsychological Studies of the Canary Islands (GENIC). In total, we generated 7548 images to simulate the access of a scan per subject every 6 months in these cohorts. We evaluated the quality of the synthetic images using six quantitative measurements and a qualitative assessment by an experienced neuroradiologist with state‐of‐the‐art results. The assumption of linear brain decay was accurate in these cohorts (R2 ∈ [.924, .940]). The experimental results show that the proposed methodology can produce anatomically plausible aging predictions that can be used to enhance longitudinal datasets. Compared to deep learning‐based generative methods, diffeomorphic registration is more likely to preserve the anatomy of the different structures of the brain, which makes it more appropriate for its use in clinical applications. The proposed methodology is able to efficiently simulate anatomically plausible 3D MRI scans of brain aging of healthy subjects from two images scanned at two different time points.
In this work, we proposed a methodology with the aim of simulating subject‐specific aging in brain magnetic resonance imaging (MRI) given two three‐dimensional images acquired at different time points. Deep learning‐based diffeomorphic registration was used as a backbone to generate deformation fields at different integration points. Similarity measurements were used for controlling the age estimation of the generated images by using a linear assumption. |
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| AbstractList | Predicting brain aging can help in the early detection and prognosis of neurodegenerative diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance imaging (MRI) have been essential to understand the structural brain changes due to aging. However, these cohorts suffer from missing data due to logistic issues in the recruitment of subjects. This paper proposes a methodology for filling up missing data in longitudinal cohorts with anatomically plausible images that capture the subject‐specific aging process. The proposed methodology is developed within the framework of diffeomorphic registration. First, two novel modules are introduced within Synthmorph, a fast, state‐of‐the‐art deep learning‐based diffeomorphic registration method, to simulate the aging process between the first and last available MRI scan for each subject in three‐dimensional (3D). The use of image registration also makes the generated images plausible by construction. Second, we used six image similarity measurements to rearrange the generated images to the specific age range. Finally, we estimated the age of every generated image by using the assumption of linear brain decay in healthy subjects. The methodology was evaluated on 2662 T1‐weighted MRI scans from 796 healthy participants from 3 different longitudinal cohorts: Alzheimer's Disease Neuroimaging Initiative, Open Access Series of Imaging Studies‐3, and Group of Neuropsychological Studies of the Canary Islands (GENIC). In total, we generated 7548 images to simulate the access of a scan per subject every 6 months in these cohorts. We evaluated the quality of the synthetic images using six quantitative measurements and a qualitative assessment by an experienced neuroradiologist with state‐of‐the‐art results. The assumption of linear brain decay was accurate in these cohorts (
R
2
∈ [.924, .940]). The experimental results show that the proposed methodology can produce anatomically plausible aging predictions that can be used to enhance longitudinal datasets. Compared to deep learning‐based generative methods, diffeomorphic registration is more likely to preserve the anatomy of the different structures of the brain, which makes it more appropriate for its use in clinical applications. The proposed methodology is able to efficiently simulate anatomically plausible 3D MRI scans of brain aging of healthy subjects from two images scanned at two different time points. Predicting brain aging can help in the early detection and prognosis of neurodegenerative diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance imaging (MRI) have been essential to understand the structural brain changes due to aging. However, these cohorts suffer from missing data due to logistic issues in the recruitment of subjects. This paper proposes a methodology for filling up missing data in longitudinal cohorts with anatomically plausible images that capture the subject-specific aging process. The proposed methodology is developed within the framework of diffeomorphic registration. First, two novel modules are introduced within Synthmorph, a fast, state-of-the-art deep learning-based diffeomorphic registration method, to simulate the aging process between the first and last available MRI scan for each subject in three-dimensional (3D). The use of image registration also makes the generated images plausible by construction. Second, we used six image similarity measurements to rearrange the generated images to the specific age range. Finally, we estimated the age of every generated image by using the assumption of linear brain decay in healthy subjects. The methodology was evaluated on 2662 T1-weighted MRI scans from 796 healthy participants from 3 different longitudinal cohorts: Alzheimer's Disease Neuroimaging Initiative, Open Access Series of Imaging Studies-3, and Group of Neuropsychological Studies of the Canary Islands (GENIC). In total, we generated 7548 images to simulate the access of a scan per subject every 6 months in these cohorts. We evaluated the quality of the synthetic images using six quantitative measurements and a qualitative assessment by an experienced neuroradiologist with state-of-the-art results. The assumption of linear brain decay was accurate in these cohorts (R ∈ [.924, .940]). The experimental results show that the proposed methodology can produce anatomically plausible aging predictions that can be used to enhance longitudinal datasets. Compared to deep learning-based generative methods, diffeomorphic registration is more likely to preserve the anatomy of the different structures of the brain, which makes it more appropriate for its use in clinical applications. The proposed methodology is able to efficiently simulate anatomically plausible 3D MRI scans of brain aging of healthy subjects from two images scanned at two different time points. Predicting brain aging can help in the early detection and prognosis of neurodegenerative diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance imaging (MRI) have been essential to understand the structural brain changes due to aging. However, these cohorts suffer from missing data due to logistic issues in the recruitment of subjects. This paper proposes a methodology for filling up missing data in longitudinal cohorts with anatomically plausible images that capture the subject‐specific aging process. The proposed methodology is developed within the framework of diffeomorphic registration. First, two novel modules are introduced within Synthmorph, a fast, state‐of‐the‐art deep learning‐based diffeomorphic registration method, to simulate the aging process between the first and last available MRI scan for each subject in three‐dimensional (3D). The use of image registration also makes the generated images plausible by construction. Second, we used six image similarity measurements to rearrange the generated images to the specific age range. Finally, we estimated the age of every generated image by using the assumption of linear brain decay in healthy subjects. The methodology was evaluated on 2662 T1‐weighted MRI scans from 796 healthy participants from 3 different longitudinal cohorts: Alzheimer's Disease Neuroimaging Initiative, Open Access Series of Imaging Studies‐3, and Group of Neuropsychological Studies of the Canary Islands (GENIC). In total, we generated 7548 images to simulate the access of a scan per subject every 6 months in these cohorts. We evaluated the quality of the synthetic images using six quantitative measurements and a qualitative assessment by an experienced neuroradiologist with state‐of‐the‐art results. The assumption of linear brain decay was accurate in these cohorts (R2 ∈ [.924, .940]). The experimental results show that the proposed methodology can produce anatomically plausible aging predictions that can be used to enhance longitudinal datasets. Compared to deep learning‐based generative methods, diffeomorphic registration is more likely to preserve the anatomy of the different structures of the brain, which makes it more appropriate for its use in clinical applications. The proposed methodology is able to efficiently simulate anatomically plausible 3D MRI scans of brain aging of healthy subjects from two images scanned at two different time points. Predicting brain aging can help in the early detection and prognosis of neurodegenerative diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance imaging (MRI) have been essential to understand the structural brain changes due to aging. However, these cohorts suffer from missing data due to logistic issues in the recruitment of subjects. This paper proposes a methodology for filling up missing data in longitudinal cohorts with anatomically plausible images that capture the subject-specific aging process. The proposed methodology is developed within the framework of diffeomorphic registration. First, two novel modules are introduced within Synthmorph, a fast, state-of-the-art deep learning-based diffeomorphic registration method, to simulate the aging process between the first and last available MRI scan for each subject in three-dimensional (3D). The use of image registration also makes the generated images plausible by construction. Second, we used six image similarity measurements to rearrange the generated images to the specific age range. Finally, we estimated the age of every generated image by using the assumption of linear brain decay in healthy subjects. The methodology was evaluated on 2662 T1-weighted MRI scans from 796 healthy participants from 3 different longitudinal cohorts: Alzheimer's Disease Neuroimaging Initiative, Open Access Series of Imaging Studies-3, and Group of Neuropsychological Studies of the Canary Islands (GENIC). In total, we generated 7548 images to simulate the access of a scan per subject every 6 months in these cohorts. We evaluated the quality of the synthetic images using six quantitative measurements and a qualitative assessment by an experienced neuroradiologist with state-of-the-art results. The assumption of linear brain decay was accurate in these cohorts (R2 ∈ [.924,.940]). The experimental results show that the proposed methodology can produce anatomically plausible aging predictions that can be used to enhance longitudinal datasets. Compared to deep learning-based generative methods, diffeomorphic registration is more likely to preserve the anatomy of the different structures of the brain, which makes it more appropriate for its use in clinical applications. The proposed methodology is able to efficiently simulate anatomically plausible 3D MRI scans of brain aging of healthy subjects from two images scanned at two different time points. Predicting brain aging can help in the early detection and prognosis of neurodegenerative diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance imaging (MRI) have been essential to understand the structural brain changes due to aging. However, these cohorts suffer from missing data due to logistic issues in the recruitment of subjects. This paper proposes a methodology for filling up missing data in longitudinal cohorts with anatomically plausible images that capture the subject-specific aging process. The proposed methodology is developed within the framework of diffeomorphic registration. First, two novel modules are introduced within Synthmorph, a fast, state-of-the-art deep learning-based diffeomorphic registration method, to simulate the aging process between the first and last available MRI scan for each subject in three-dimensional (3D). The use of image registration also makes the generated images plausible by construction. Second, we used six image similarity measurements to rearrange the generated images to the specific age range. Finally, we estimated the age of every generated image by using the assumption of linear brain decay in healthy subjects. The methodology was evaluated on 2662 T1-weighted MRI scans from 796 healthy participants from 3 different longitudinal cohorts: Alzheimer's Disease Neuroimaging Initiative, Open Access Series of Imaging Studies-3, and Group of Neuropsychological Studies of the Canary Islands (GENIC). In total, we generated 7548 images to simulate the access of a scan per subject every 6 months in these cohorts. We evaluated the quality of the synthetic images using six quantitative measurements and a qualitative assessment by an experienced neuroradiologist with state-of-the-art results. The assumption of linear brain decay was accurate in these cohorts (R2 ∈ [.924, .940]). The experimental results show that the proposed methodology can produce anatomically plausible aging predictions that can be used to enhance longitudinal datasets. Compared to deep learning-based generative methods, diffeomorphic registration is more likely to preserve the anatomy of the different structures of the brain, which makes it more appropriate for its use in clinical applications. The proposed methodology is able to efficiently simulate anatomically plausible 3D MRI scans of brain aging of healthy subjects from two images scanned at two different time points.Predicting brain aging can help in the early detection and prognosis of neurodegenerative diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance imaging (MRI) have been essential to understand the structural brain changes due to aging. However, these cohorts suffer from missing data due to logistic issues in the recruitment of subjects. This paper proposes a methodology for filling up missing data in longitudinal cohorts with anatomically plausible images that capture the subject-specific aging process. The proposed methodology is developed within the framework of diffeomorphic registration. First, two novel modules are introduced within Synthmorph, a fast, state-of-the-art deep learning-based diffeomorphic registration method, to simulate the aging process between the first and last available MRI scan for each subject in three-dimensional (3D). The use of image registration also makes the generated images plausible by construction. Second, we used six image similarity measurements to rearrange the generated images to the specific age range. Finally, we estimated the age of every generated image by using the assumption of linear brain decay in healthy subjects. The methodology was evaluated on 2662 T1-weighted MRI scans from 796 healthy participants from 3 different longitudinal cohorts: Alzheimer's Disease Neuroimaging Initiative, Open Access Series of Imaging Studies-3, and Group of Neuropsychological Studies of the Canary Islands (GENIC). In total, we generated 7548 images to simulate the access of a scan per subject every 6 months in these cohorts. We evaluated the quality of the synthetic images using six quantitative measurements and a qualitative assessment by an experienced neuroradiologist with state-of-the-art results. The assumption of linear brain decay was accurate in these cohorts (R2 ∈ [.924, .940]). The experimental results show that the proposed methodology can produce anatomically plausible aging predictions that can be used to enhance longitudinal datasets. Compared to deep learning-based generative methods, diffeomorphic registration is more likely to preserve the anatomy of the different structures of the brain, which makes it more appropriate for its use in clinical applications. The proposed methodology is able to efficiently simulate anatomically plausible 3D MRI scans of brain aging of healthy subjects from two images scanned at two different time points. Predicting brain aging can help in the early detection and prognosis of neurodegenerative diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance imaging (MRI) have been essential to understand the structural brain changes due to aging. However, these cohorts suffer from missing data due to logistic issues in the recruitment of subjects. This paper proposes a methodology for filling up missing data in longitudinal cohorts with anatomically plausible images that capture the subject‐specific aging process. The proposed methodology is developed within the framework of diffeomorphic registration. First, two novel modules are introduced within Synthmorph, a fast, state‐of‐the‐art deep learning‐based diffeomorphic registration method, to simulate the aging process between the first and last available MRI scan for each subject in three‐dimensional (3D). The use of image registration also makes the generated images plausible by construction. Second, we used six image similarity measurements to rearrange the generated images to the specific age range. Finally, we estimated the age of every generated image by using the assumption of linear brain decay in healthy subjects. The methodology was evaluated on 2662 T1‐weighted MRI scans from 796 healthy participants from 3 different longitudinal cohorts: Alzheimer's Disease Neuroimaging Initiative, Open Access Series of Imaging Studies‐3, and Group of Neuropsychological Studies of the Canary Islands (GENIC). In total, we generated 7548 images to simulate the access of a scan per subject every 6 months in these cohorts. We evaluated the quality of the synthetic images using six quantitative measurements and a qualitative assessment by an experienced neuroradiologist with state‐of‐the‐art results. The assumption of linear brain decay was accurate in these cohorts (R2 ∈ [.924, .940]). The experimental results show that the proposed methodology can produce anatomically plausible aging predictions that can be used to enhance longitudinal datasets. Compared to deep learning‐based generative methods, diffeomorphic registration is more likely to preserve the anatomy of the different structures of the brain, which makes it more appropriate for its use in clinical applications. The proposed methodology is able to efficiently simulate anatomically plausible 3D MRI scans of brain aging of healthy subjects from two images scanned at two different time points. In this work, we proposed a methodology with the aim of simulating subject‐specific aging in brain magnetic resonance imaging (MRI) given two three‐dimensional images acquired at different time points. Deep learning‐based diffeomorphic registration was used as a backbone to generate deformation fields at different integration points. Similarity measurements were used for controlling the age estimation of the generated images by using a linear assumption. Predicting brain aging can help in the early detection and prognosis of neurodegenerative diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance imaging (MRI) have been essential to understand the structural brain changes due to aging. However, these cohorts suffer from missing data due to logistic issues in the recruitment of subjects. This paper proposes a methodology for filling up missing data in longitudinal cohorts with anatomically plausible images that capture the subject‐specific aging process. The proposed methodology is developed within the framework of diffeomorphic registration. First, two novel modules are introduced within Synthmorph, a fast, state‐of‐the‐art deep learning‐based diffeomorphic registration method, to simulate the aging process between the first and last available MRI scan for each subject in three‐dimensional (3D). The use of image registration also makes the generated images plausible by construction. Second, we used six image similarity measurements to rearrange the generated images to the specific age range. Finally, we estimated the age of every generated image by using the assumption of linear brain decay in healthy subjects. The methodology was evaluated on 2662 T1‐weighted MRI scans from 796 healthy participants from 3 different longitudinal cohorts: Alzheimer's Disease Neuroimaging Initiative, Open Access Series of Imaging Studies‐3, and Group of Neuropsychological Studies of the Canary Islands (GENIC). In total, we generated 7548 images to simulate the access of a scan per subject every 6 months in these cohorts. We evaluated the quality of the synthetic images using six quantitative measurements and a qualitative assessment by an experienced neuroradiologist with state‐of‐the‐art results. The assumption of linear brain decay was accurate in these cohorts ( R 2 ∈ [.924, .940]). The experimental results show that the proposed methodology can produce anatomically plausible aging predictions that can be used to enhance longitudinal datasets. Compared to deep learning‐based generative methods, diffeomorphic registration is more likely to preserve the anatomy of the different structures of the brain, which makes it more appropriate for its use in clinical applications. The proposed methodology is able to efficiently simulate anatomically plausible 3D MRI scans of brain aging of healthy subjects from two images scanned at two different time points. In this work, we proposed a methodology with the aim of simulating subject‐specific aging in brain magnetic resonance imaging (MRI) given two three‐dimensional images acquired at different time points. Deep learning‐based diffeomorphic registration was used as a backbone to generate deformation fields at different integration points. Similarity measurements were used for controlling the age estimation of the generated images by using a linear assumption. |
| Author | Ferreira, Daniel Tzortzakakis, Antonios Moreno, Rodrigo Westman, Eric Fu, Jingru Barroso, José |
| AuthorAffiliation | 5 Division of Clinical Geriatrics Centre for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society (NVS), Karolinska Institutet Stockholm Sweden 2 Division of Radiology Department for Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet Stockholm Sweden 6 Department of Neuroimaging Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London London United Kingdom 1 Division of Biomedical Imaging Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology Stockholm Sweden 3 Medical Radiation Physics and Nuclear Medicine Functional Unit of Nuclear Medicine, Karolinska University Hospital Huddinge Stockholm Sweden 4 Department of Psychology Faculty of Health Sciences, University Fernando Pessoa Canarias Las Palmas Spain |
| AuthorAffiliation_xml | – name: 3 Medical Radiation Physics and Nuclear Medicine Functional Unit of Nuclear Medicine, Karolinska University Hospital Huddinge Stockholm Sweden – name: 6 Department of Neuroimaging Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London London United Kingdom – name: 1 Division of Biomedical Imaging Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology Stockholm Sweden – name: 5 Division of Clinical Geriatrics Centre for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society (NVS), Karolinska Institutet Stockholm Sweden – name: 2 Division of Radiology Department for Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet Stockholm Sweden – name: 4 Department of Psychology Faculty of Health Sciences, University Fernando Pessoa Canarias Las Palmas Spain |
| Author_xml | – sequence: 1 givenname: Jingru orcidid: 0000-0003-4175-395X surname: Fu fullname: Fu, Jingru email: jingruf@kth.se organization: Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology – sequence: 2 givenname: Antonios orcidid: 0000-0001-7563-732X surname: Tzortzakakis fullname: Tzortzakakis, Antonios organization: Functional Unit of Nuclear Medicine, Karolinska University Hospital – sequence: 3 givenname: José surname: Barroso fullname: Barroso, José organization: Faculty of Health Sciences, University Fernando Pessoa Canarias – sequence: 4 givenname: Eric orcidid: 0000-0002-3115-2977 surname: Westman fullname: Westman, Eric organization: Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London – sequence: 5 givenname: Daniel orcidid: 0000-0001-9522-4338 surname: Ferreira fullname: Ferreira, Daniel organization: Centre for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society (NVS), Karolinska Institutet – sequence: 6 givenname: Rodrigo orcidid: 0000-0001-5765-2964 surname: Moreno fullname: Moreno, Rodrigo organization: Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36468536$$D View this record in MEDLINE/PubMed https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-328720$$DView record from Swedish Publication Index http://kipublications.ki.se/Default.aspx?queryparsed=id:151441296$$DView record from Swedish Publication Index |
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| CitedBy_id | crossref_primary_10_1016_j_neuroimage_2023_120469 crossref_primary_10_48084_etasr_8728 crossref_primary_10_1145_3707462 |
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| ContentType | Journal Article |
| Copyright | 2022 The Authors. published by Wiley Periodicals LLC. 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. 2022. This article is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| CorporateAuthor | for the Alzheimer's Disease Neuroimaging Initiative Alzheimer's Disease Neuroimaging Initiative |
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| Keywords | diffeomorphic registration brain aging medical image generation synthetic brain aging |
| Language | English |
| License | Attribution-NonCommercial-NoDerivs 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
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| Notes | Funding information As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at Digital Futures, Grant/Award Number: project dBrain; VINNOVA, Grant/Award Number: 2108; Alzheimer's Disease Neuroimaging Initiative; National Institutes of Health, Grant/Award Number: U01 AG024904; DOD ADNI, Grant/Award Number: W81XWH‐12‐2‐0012; National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; AbbVie; Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol‐Myers Squibb; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EUROIMMUN Medizinische Labordiagnostika AG; F. Hoffmann‐La Roche Ltd; Genentech, Inc.; Fujirebio US; GE Healthcare; IXICO Ltd; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; Transition Therapeutics; Canadian Institutes of Health Research; China Scholarship Council; Center for Innovative Medicine (CIMED); Stockholm County Council and Karolinska Institutet; Hjärnfonden; Alzheimerfonden; Demensfonden; Neurofonden; Stiftelsen För Gamla Tjänarinnor; Fundación Canaria Dr Manuel Morales; Fundación Cajacanarias; Estrategia de Especialización Inteligente de Canarias RIS3 from Consejería de Economía; Industria, Comercio y Conocimiento del Gobierno de Canarias; Programa Operativo FEDER Canarias 2014–2020, Grant/Award Number: ProID2020010063; Barncancerfonden, Grant/Award Number: MT2019‐0019 Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf https://adni.loni.usc.edu/ ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (https://adni.loni.usc.edu/). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf Funding information Digital Futures, Grant/Award Number: project dBrain; VINNOVA, Grant/Award Number: 2108; Alzheimer's Disease Neuroimaging Initiative; National Institutes of Health, Grant/Award Number: U01 AG024904; DOD ADNI, Grant/Award Number: W81XWH‐12‐2‐0012; National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; AbbVie; Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol‐Myers Squibb; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EUROIMMUN Medizinische Labordiagnostika AG; F. Hoffmann‐La Roche Ltd; Genentech, Inc.; Fujirebio US; GE Healthcare; IXICO Ltd; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; Transition Therapeutics; Canadian Institutes of Health Research; China Scholarship Council; Center for Innovative Medicine (CIMED); Stockholm County Council and Karolinska Institutet; Hjärnfonden; Alzheimerfonden; Demensfonden; Neurofonden; Stiftelsen För Gamla Tjänarinnor; Fundación Canaria Dr Manuel Morales; Fundación Cajacanarias; Estrategia de Especialización Inteligente de Canarias RIS3 from Consejería de Economía; Industria, Comercio y Conocimiento del Gobierno de Canarias; Programa Operativo FEDER Canarias 2014–2020, Grant/Award Number: ProID2020010063; Barncancerfonden, Grant/Award Number: MT2019‐0019 |
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| SubjectTerms | Age Aging Algorithms Alzheimer's disease Artificial intelligence Biomarkers Brain Brain - diagnostic imaging Brain - pathology brain aging Brain research Decay Deep learning diffeomorphic registration Evaluation Humans Image processing Image Processing, Computer-Assisted - methods Image quality Image registration Imaging, Three-Dimensional - methods Magnetic resonance imaging Magnetic Resonance Imaging - methods medical image generation Medical imaging Methodology Missing data Neural networks Neurodegenerative diseases Neuroimaging Neuroimaging - methods Registration Simulation synthetic brain aging |
| Title | Fast three‐dimensional image generation for healthy brain aging using diffeomorphic registration |
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