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...

Full description

Bibliographic Details
Main Authors: Grace Wen, Vickie Shim, Samantha Jane Holdsworth, Justin Fernandez, Miao Qiao, Nikola Kasabov, Alan Wang
Format: Article
Language:English
Published: MDPI AG 2023-03-01
Series:Bioengineering
Subjects:
Online Access:https://www.mdpi.com/2306-5354/10/4/397
_version_ 1797606384704946176
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.
first_indexed 2024-03-11T05:14:26Z
format Article
id doaj.art-cf7e57cd907642ff911c9d94bf861c40
institution Directory Open Access Journal
issn 2306-5354
language English
last_indexed 2024-03-11T05:14:26Z
publishDate 2023-03-01
publisher MDPI AG
record_format Article
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
work_keys_str_mv AT gracewen machinelearningforbrainmridataharmonisationasystematicreview
AT vickieshim machinelearningforbrainmridataharmonisationasystematicreview
AT samanthajaneholdsworth machinelearningforbrainmridataharmonisationasystematicreview
AT justinfernandez machinelearningforbrainmridataharmonisationasystematicreview
AT miaoqiao machinelearningforbrainmridataharmonisationasystematicreview
AT nikolakasabov machinelearningforbrainmridataharmonisationasystematicreview
AT alanwang machinelearningforbrainmridataharmonisationasystematicreview