MRS‐Sim: Open‐Source Framework for Simulating In Vivo‐Like Magnetic Resonance Spectra

Realistic, in vivo-like synthetic data is increasingly needed to develop and validate methods in magnetic resonance spectroscopy. MRS-Sim is a powerful, open-source framework for simulating such data while providing known ground truth values. Its modularity enables modeling the complexities of MRS data for various in vivo scenarios. The underlying physical equations include both commonly used spectral components of linear-combination fitting routines and two novel components. The first is a 3D <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>B</mml:mi> <mml:mn>0</mml:mn></mml:msub> </mml:mrow> <mml:annotation>$$ {B}_0 $$</mml:annotation></mml:semantics> </mml:math> field map simulator that models <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msub><mml:mi>B</mml:mi> <mml:mn>0</mml:mn></mml:msub> </mml:mrow> <mml:annotation>$$ {B}_0 $$</mml:annotation></mml:semantics> </mml:math> field inhomogeneities, ranging from slight variations to severe distortions. The second is a novel semi-parametric generator that mimics signals from poorly characterized residual water regions and spectral baseline contributions. This framework can simulate scenarios ranging from raw multi-coil transients to preprocessed, coil-combined multiaverage data. Simulating realistic in vivo-like datasets requires appropriate model parameter ranges and distributions, best determined by analyzing the fitting parameters from existing in vivo data. Therefore, MRS-Sim includes tools for analyzing the ranges and statistical distributions of those parameters from in vivo datasets fitted with Osprey, allowing simulations to be tailored to specific datasets. Additionally, the accompanying repository of supplemental information assists nonexpert users with general simulations of MRS data. The modularity of this framework facilitates easy customization in various in vivo scenarios and promotes continued community development. Using a single framework for diverse applications addresses the inconsistencies in current protocols. By simulating in vivo-like data, MRS-Sim supports many MRS tasks, including verifying spectral fitting protocols and conducting reproducibility analyses. Readily available synthetic data also benefits deep learning research, particularly when sufficient in vivo data is unavailable for training. Overall, MRS-Sim will promote reproducibility and make MRS research more accessible to a wider audience.