Comparison of Variational Autoencoders for Magnetic Resonance Spectroscopy Data Synthesis

Motivation: Scarcity of MRS datasets hinders deep learning model development, often leading to reliance on simulations which show difficulties with replicating in-vivo characteristics. Goal(s): The goal is to evaluate different variational autoencoders to enhance MRS datasets and improve deep learning model development for MRS. Approach: This study assesses different models for generating MRS data. Additionally, interpolation is done between multiple pairs of real spectra to improve the diversity of the synthetic data. Results: One model shows great potential in terms of reconstruction quality and generative performance. The incorporation of interpolation further enhances the diversity in synthetic spectra, particularly in relation to residual water signals. Impact: The demonstrated potential of variational autoencoders for MRS data generation will help in generating synthetic data that is similar to in-vivo data. This will help the development of other deep learning models for MRS applications.