Conditional VAE for Single-Voxel MRS Data Generation

We propose a conditional VAE to synthesize single-voxel MRS data. This deep learning method can be used to enrich in-vivo datasets for other machine learning applications, without using any physics-based models. Our work is a proof-of-concept study which demonstrates the potential of a cVAE for MRS data generation by using a synthetic dataset of 8,000 spectra for training. We evaluate our model by performing a linear interpolation of the latent space, which shows that spectral properties are captured in the latent space, meaning that our model can learn spectral features from the data and can generate new samples.