English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This literature review presents a comprehensive overview of machine learning(ML) applications in proton magnetic resonance spectroscopy (MRS). As the useof ML techniques in MRS continues to grow, this review aims to provide the MRScommunity with a structured overview of the state-of-the-art methods.Specifically, we examine and summarize studies published between 2017 and 2023from major journals in the magnetic resonance field. We categorize thesestudies based on a typical MRS workflow, including data acquisition,processing, analysis, and artificial data generation. Our review reveals thatML in MRS is still in its early stages, with a primary focus on processing andanalysis techniques, and less attention given to data acquisition. We alsofound that many studies use similar model architectures, with little comparisonto alternative architectures. Additionally, the generation of artificial datais a crucial topic, with no consistent method for its generation. Furthermore,many studies demonstrate that artificial data suffers from generalizationissues when tested on in-vivo data. We also conclude that risks related to MLmodels 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 fromthe reviewed studies justify further research in this field.

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