A Review of Machine Learning Applications for the Proton Magnetic Resonance Spectroscopy Workflow

TL;DR

This review summarizes recent machine learning applications in proton magnetic resonance spectroscopy, highlighting current trends, challenges, and the need for standardized methods and addressing generalization and bias issues for clinical use.

Contribution

It provides a structured overview of ML methods in MRS from 2017 to 2023, identifying gaps and future directions in data acquisition, artificial data generation, and model robustness.

Findings

ML in MRS is in early stages, mainly focusing on processing and analysis.

Many studies use similar ML architectures with limited comparison.

Artificial data generation faces generalization and bias challenges.

Abstract

This literature review presents a comprehensive overview of machine learning (ML) applications in proton magnetic resonance spectroscopy (MRS). As the use of ML techniques in MRS continues to grow, this review aims to provide the MRS community with a structured overview of the state-of-the-art methods. Specifically, we examine and summarize studies published between 2017 and 2023 from major journals in the magnetic resonance field. We categorize these studies based on a typical MRS workflow, including data acquisition, processing, analysis, and artificial data generation. Our review reveals that ML in MRS is still in its early stages, with a primary focus on processing and analysis techniques, and less attention given to data acquisition. We also found that many studies use similar model architectures, with little comparison to alternative architectures. Additionally, the generation of artificial data is a crucial topic, with no consistent method for its generation. Furthermore, many studies demonstrate that artificial data suffers from generalization issues when tested on in-vivo data. We also conclude that risks related to ML models should be addressed, particularly for clinical applications. Therefore, output uncertainty measures and model biases are critical to investigate. Nonetheless, the rapid development of ML in MRS and the promising results from the reviewed studies justify further research in this field.