Machine Learning for Brain MRI Data Harmonisation: A Systematic Review.

ABSTRACT:

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 Ju

SUBMITTER: Wen G 

PROVIDER: S-EPMC10135601 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature