Synthetic Data in MR Spectroscopy: Current Practices, Applications, and Considerations

TL;DR

This paper reviews current practices and considerations for using synthetic data in Magnetic Resonance Spectroscopy, highlighting its benefits for research, validation, and addressing data scarcity in clinical applications.

Contribution

It provides a comprehensive review and evaluation of synthetic data generation and use in MRS, including collective insights and methodologies from experts.

Findings

Synthetic data enhances reproducibility and validation in MRS.

It addresses limited data availability for clinical populations.

Current practices include various generation and application methods.

Abstract

The use of synthetic data has emerged as an essential tool in Magnetic Resonance Spectroscopy (MRS) research and applications, providing advantages for optimization of acquisition, software validation, deep learning applications, and enhanced reproducibility. Importantly, synthetic data addresses challenges of limited training data availability, particularly for clinical populations, and offers controlled solutions for investigating uncertainties and unexplained variance with in vivo data. This work provides a review and evaluation of current practices in the use and generation of synthetic data within the MRS field. Conducted by the MRS Synthetic Data Working Group under the Code & Data Sharing Committee of the MRS Study Group of the International Society for Magnetic Resonance in Medicine (ISMRM), this manuscript encompasses existing literature, supplemented by collective experience and in-house methodologies.