Magnetic Resonance Spectroscopy Data Generation Using Physics-informed Autoencoders

Motivation: MRS data can be accurately simulated in terms of metabolite signals, but contributions from macromolecules, lipids, and scan-related imperfections are more challenging to simulate, leading to realism gaps between in-vivo and simulated spectra. Goal(s): The goal is to bridge the realism gap between in-vivo and simulated MRS spectra for developing downstream deep learning applications. Approach: We propose a physics-informed autoencoder which uses signal-based modules in the encoder and a deep learning-based decoder to generate spectra with in-vivo characteristics. Results: Our physics-informed method effectively narrows the realism gap between in-vivo and simulated spectra with reduced reconstruction scores and increased overlap in spectral feature space. Impact: Our research lays the foundation for a robust hybrid MRS data generation framework which generates realistic MRS data while maintaining the interpretability of physics-based simulations. It will help to generate data for developing downstream deep learning applications for MRS.