Artifact Simulation Toolbox for GABA-Edited Magnetic Resonance Spectroscopy

Motivation: GABA-edited Magnetic Resonance Spectroscopy (MRS) is a valuable tool used to measure GABA. However, it suffers from low signal to noise ratio. Machine learning has been recently proposed to overcome these challenges but accessing the large amount of in vivo data necessary for training can be difficult. Goal(s): To create a GABA-edited MRS artifact toolbox. Approach: We developed an open-access python toolbox to simulate four common artifacts (ghosting, eddy current effects, lipid contamination and phase/frequency shifts) in GABA-edited MRS. Results: The toolbox will support machine learning algorithm development by complementing existing simulation software and allow for flexible user inputs for data personalization. Impact: Our open-access python toolbox can be used to simulate spurious echoes, eddy currents, lipid contamination and motion artifacts to provide realistic and representative GABA-edited MRS data. This can be used for methods development such as training machine learning algorithms.