[2603.00736] Data-driven Synthesis of Magnetic Resonance Spectroscopy Data using a Variational Autoencoder

Physics > Medical Physics arXiv:2603.00736 (physics) [Submitted on 28 Feb 2026] Title:Data-driven Synthesis of Magnetic Resonance Spectroscopy Data using a Variational Autoencoder Authors:Dennis M.J. van de Sande, Julian P. Merkofer, Sina Amirrajab, Mitko Veta, Gerhard S. Drenthen, Jacobus F.A. Jansen, Marcel Breeuwer View a PDF of the paper titled Data-driven Synthesis of Magnetic Resonance Spectroscopy Data using a Variational Autoencoder, by Dennis M.J. van de Sande and 6 other authors View PDF HTML (experimental) Abstract:The development of deep learning methods for magnetic resonance spectroscopy (MRS) is often hindered by limited availability of large, high-quality training datasets. While physics-based simulations are commonly used to mitigate this limitation, accurately modeling all in-vivo signal components remains challenging. In this work, we propose a data-driven framework for synthesizing in-vivo MRS data using a variational autoencoder (VAE) trained exclusively on measured single-voxel spectroscopy data. The model learns a low-dimensional latent representation of complex-valued spectra and enables generation of new samples through latent-space sampling and interpolation. The generative performance of the proposed approach is evaluated using a comprehensive set of complementary analyses, including reconstruction quality, feature-level similarity using low-dimensional embeddings, application-based signal quality metrics, and metabolite quantification agreement....