Machine Learning for Brain MRI Data Harmonisation: A Systematic Review
Background: Magnetic Resonance Imaging (MRI) data collected from multiple centres can be heterogeneous due to factors such as the scanner used and the site location. To reduce this heterogeneity, the data needs to be harmonised. In recent years, machine learning (ML) has been used to solve different...
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MDPI AG
2023-03-01
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Online Access: | https://www.mdpi.com/2306-5354/10/4/397 |
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author | Grace Wen Vickie Shim Samantha Jane Holdsworth Justin Fernandez Miao Qiao Nikola Kasabov Alan Wang |
author_facet | Grace Wen Vickie Shim Samantha Jane Holdsworth Justin Fernandez Miao Qiao Nikola Kasabov Alan Wang |
author_sort | Grace Wen |
collection | DOAJ |
description | Background: Magnetic Resonance Imaging (MRI) data collected from multiple centres can be heterogeneous due to factors such as the scanner used and the site location. To reduce this heterogeneity, the data needs to be harmonised. In recent years, machine learning (ML) has been used to solve different types of problems related to MRI data, showing great promise. Objective: This study explores how well various ML algorithms perform in harmonising MRI data, both implicitly and explicitly, by summarising the findings in relevant peer-reviewed articles. Furthermore, it provides guidelines for the use of current methods and identifies potential future research directions. Method: This review covers articles published through PubMed, Web of Science, and IEEE databases through June 2022. Data from studies were analysed based on the criteria of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Quality assessment questions were derived to assess the quality of the included publications. Results: a total of 41 articles published between 2015 and 2022 were identified and analysed. In the review, MRI data has been found to be harmonised either in an implicit (<i>n</i> = 21) or an explicit (<i>n</i> = 20) way. Three MRI modalities were identified: structural MRI (<i>n</i> = 28), diffusion MRI (<i>n</i> = 7) and functional MRI (<i>n</i> = 6). Conclusion: Various ML techniques have been employed to harmonise different types of MRI data. There is currently a lack of consistent evaluation methods and metrics used across studies, and it is recommended that the issue be addressed in future studies. Harmonisation of MRI data using ML shows promises in improving performance for ML downstream tasks, while caution should be exercised when using ML-harmonised data for direct interpretation. |
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institution | Directory Open Access Journal |
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language | English |
last_indexed | 2024-03-11T05:14:26Z |
publishDate | 2023-03-01 |
publisher | MDPI AG |
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series | Bioengineering |
spelling | doaj.art-cf7e57cd907642ff911c9d94bf861c402023-11-17T18:21:32ZengMDPI AGBioengineering2306-53542023-03-0110439710.3390/bioengineering10040397Machine Learning for Brain MRI Data Harmonisation: A Systematic ReviewGrace Wen0Vickie Shim1Samantha Jane Holdsworth2Justin Fernandez3Miao Qiao4Nikola Kasabov5Alan Wang6Auckland Bioengineering Institute, University of Auckland, Auckland 1142, New ZealandAuckland Bioengineering Institute, University of Auckland, Auckland 1142, New ZealandCentre for Brain Research, University of Auckland, Auckland 1142, New ZealandAuckland Bioengineering Institute, University of Auckland, Auckland 1142, New ZealandDepartment of Computer Science, University of Auckland, Auckland 1142, New ZealandAuckland Bioengineering Institute, University of Auckland, Auckland 1142, New ZealandAuckland Bioengineering Institute, University of Auckland, Auckland 1142, New ZealandBackground: Magnetic Resonance Imaging (MRI) data collected from multiple centres can be heterogeneous due to factors such as the scanner used and the site location. To reduce this heterogeneity, the data needs to be harmonised. In recent years, machine learning (ML) has been used to solve different types of problems related to MRI data, showing great promise. Objective: This study explores how well various ML algorithms perform in harmonising MRI data, both implicitly and explicitly, by summarising the findings in relevant peer-reviewed articles. Furthermore, it provides guidelines for the use of current methods and identifies potential future research directions. Method: This review covers articles published through PubMed, Web of Science, and IEEE databases through June 2022. Data from studies were analysed based on the criteria of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Quality assessment questions were derived to assess the quality of the included publications. Results: a total of 41 articles published between 2015 and 2022 were identified and analysed. In the review, MRI data has been found to be harmonised either in an implicit (<i>n</i> = 21) or an explicit (<i>n</i> = 20) way. Three MRI modalities were identified: structural MRI (<i>n</i> = 28), diffusion MRI (<i>n</i> = 7) and functional MRI (<i>n</i> = 6). Conclusion: Various ML techniques have been employed to harmonise different types of MRI data. There is currently a lack of consistent evaluation methods and metrics used across studies, and it is recommended that the issue be addressed in future studies. Harmonisation of MRI data using ML shows promises in improving performance for ML downstream tasks, while caution should be exercised when using ML-harmonised data for direct interpretation.https://www.mdpi.com/2306-5354/10/4/397systematic reviewharmonisationnormalisationstandardisationMRIimage pre-processing |
spellingShingle | Grace Wen Vickie Shim Samantha Jane Holdsworth Justin Fernandez Miao Qiao Nikola Kasabov Alan Wang Machine Learning for Brain MRI Data Harmonisation: A Systematic Review Bioengineering systematic review harmonisation normalisation standardisation MRI image pre-processing |
title | Machine Learning for Brain MRI Data Harmonisation: A Systematic Review |
title_full | Machine Learning for Brain MRI Data Harmonisation: A Systematic Review |
title_fullStr | Machine Learning for Brain MRI Data Harmonisation: A Systematic Review |
title_full_unstemmed | Machine Learning for Brain MRI Data Harmonisation: A Systematic Review |
title_short | Machine Learning for Brain MRI Data Harmonisation: A Systematic Review |
title_sort | machine learning for brain mri data harmonisation a systematic review |
topic | systematic review harmonisation normalisation standardisation MRI image pre-processing |
url | https://www.mdpi.com/2306-5354/10/4/397 |
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