Wen Grace, Shim Vickie, Holdsworth Samantha Jane, Fernandez Justin, Qiao Miao, Kasabov Nikola, Wang Alan
Auckland Bioengineering Institute, University of Auckland, Auckland 1142, New Zealand.
Centre for Brain Research, University of Auckland, Auckland 1142, New Zealand.
Bioengineering (Basel). 2023 Mar 23;10(4):397. doi: 10.3390/bioengineering10040397.
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.
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.
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.
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 ( = 21) or an explicit ( = 20) way. Three MRI modalities were identified: structural MRI ( = 28), diffusion MRI ( = 7) and functional MRI ( = 6).
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.
由于所使用的扫描仪和站点位置等因素，从多个中心收集的磁共振成像（MRI）数据可能存在异质性。为了减少这种异质性，需要对数据进行协调。近年来，机器学习（ML）已被用于解决与MRI数据相关的不同类型问题，并显示出巨大的前景。
本研究通过总结相关同行评审文章中的发现，探讨各种ML算法在隐式和显式协调MRI数据方面的表现。此外，它还提供了当前方法的使用指南，并确定了未来潜在的研究方向。
本综述涵盖截至2022年6月通过PubMed、科学网和IEEE数据库发表的文章。根据系统评价和Meta分析的首选报告项目（PRISMA）标准对研究数据进行分析。导出质量评估问题以评估纳入出版物的质量。
共识别并分析了2015年至2022年间发表的41篇文章。在综述中，发现MRI数据以隐式（=21）或显式（=20）方式进行了协调。确定了三种MRI模态：结构MRI（=28）、扩散MRI（=7）和功能MRI（=6）。
已采用各种ML技术来协调不同类型的MRI数据。目前各研究缺乏一致的评估方法和指标，建议在未来研究中解决这一问题。使用ML协调MRI数据在提高ML下游任务的性能方面显示出前景，但在将ML协调的数据用于直接解释时应谨慎。
