A Digital Phantom for MR Spectroscopy Data Simulation
Simulated data is increasingly valued for validating MR spectroscopy (MRS) processing and analysis algorithms, but there is no consensus on the optimal simulation models or parameters. This work introduces a novel MRS digital brain phantom framework, a comprehensive and modular foundation for MRS data simulation, generated by combining anatomical and tissue label information with literature metabolite data. Paired with a signal-based model, the phantom generates various spectral datasets, saved in the NIfTI-MRS format for downstream use. Implemented across two anatomical templates at different resolutions and tested over a range of user-defined parameters, the phantom produced spectra with realistic signal characteristics and structural variation, validated against in vivo MRS data acquired under similar conditions.
Abstract
Purpose: 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.
Methods: 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.
Results: 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 variation.