A Digital Phantom for MR Spectroscopy Data Simulation

TL;DR

This paper introduces a modular, open-source digital brain phantom framework for MR Spectroscopy data simulation, enabling realistic, diverse spectral datasets for algorithm validation and robustness testing.

Contribution

It presents a novel, comprehensive MRS digital phantom framework combining anatomical, tissue, and metabolite data, with a user-friendly GUI and open-source code.

Findings

Simulated spectra closely match in-vivo data in shape and SNR

The phantom captures key variability features for robustness testing

Outputs are compatible with downstream analysis tools

Abstract

Simulated data is increasingly valued by researchers for validating MRS processing and analysis algorithms. However, there is no consensus on the optimal approaches for simulation models and parameters. This study introduces a novel MRS digital brain phantom framework, providing a comprehensive and modular foundation for MRS data simulation. The framework generates a digital brain phantom by combining anatomical and tissue label information with metabolite data from the literature. This phantom contains all necessary information for simulating spectral data. The MRS phantom is combined with a signal-based model to demonstrate its functionality and usability in generating various spectral datasets. Outputs can be saved in the NIfTI-MRS format, enabling their use in downstream applications. To evaluate the realism of the simulated spectra, a comparison was performed against in-vivo MRS data acquired under similar conditions. The phantom was implemented using two anatomical templates at different resolutions and tested across a range of user-defined simulation parameters. Simulated spectra exhibited realistic signal characteristics and structural variability. When compared to in-vivo data, the simulated spectra closely matched in terms of spectral shape, signal-to-noise ratio, and metabolite quantification. The simulations also captured key variability features and provided additional diversity not present in the in-vivo dataset, supporting use in robustness testing and data augmentation. This novel digital phantom provides a flexible and extensible platform for MRS data simulation. Its modular architecture, user-friendly GUI, and open-source implementation support reproducible research, algorithm development, and validation in the MRS community.