Data-Driven Synthesis of Magnetic Resonance Spectroscopy Data Using a Variational Autoencoder

The development of deep learning methods for magnetic resonance spectroscopy (MRS) is often hindered by limited availability of large, high-quality training datasets. We propose using a variational autoencoder trained on measured spectroscopy data to generate synthetic samples. Our approach learns a compressed representation of complex spectra and can create new samples through latent-space operations. Testing revealed the model successfully reconstructs dominant spectral patterns and generates synthetic data matching in-vivo characteristics. When augmenting limited data subsets, synthetic spectra improved noise and signal quality metrics. However, the results also reveal limitations of the generative approach, including under-representation of stochastic noise and reduced accuracy in absolute metabolite quantification. The work establishes an evaluation framework emphasizing that validation should consider downstream applications when employing synthetic data.