Machine Learning for Brain MRI Data Harmonisation: A Systematic Review

Grace Wen, Vickie Shim, Samantha Jane Holdsworth, Justin Fernandez, Miao Qiao, Nikola Kasabov, Alan Wang

Research output: Contribution to journalReview articlepeer-review

3 Citations (Scopus)
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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 (n = 21) or an explicit (n = 20) way. Three MRI modalities were identified: structural MRI (n = 28), diffusion MRI (n = 7) and functional MRI (n = 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.
Original languageEnglish
Article number397
Pages (from-to)1-20
Number of pages20
Issue number4
Early online date23 Mar 2023
Publication statusPublished online - 23 Mar 2023

Bibliographical note

Funding Information:
We extend our gratitude to the MBIE Catalyst: Strategic Fund NZ-Singapore Data Science Research Programme UOAX2001 for the financial support, which enabled the contributions of Miao Qiao, Vickie Shim, Samantha Holdsworth, Alan Wang, and Grace Wen to this research project. We would also like to thank the University of Auckland and the Auckland Bioengineering Institute for their generous support.

Funding Information:
This work was partially supported by the Ministry of Business, Innovation and Employment (MBIE) of New Zealand [grant number PROP70240-CNZSDS-UOA] and the Health Research Council of New Zealand [grant number 21/144].

Publisher Copyright:
© 2023 by the authors.


  • systematic review
  • image pre-processing
  • standardisation
  • MRI
  • harmonisation
  • normalisation


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