Quantification of in vivo magnetic resonance spectroscopy data with pyAMARES, an open-source python library for flexible and robust MRS fitting

Motivation: As a mainstream time-domain fitting algorithm for quantifying MRS data, to date, AMARES has been confined to Java and MATLAB, while the dominant language for deep learning, Python, does not yet have an AMARES implementation. Goal(s): To develop pyAMARES, a python package implementing the AMARES, providing the MRS community with flexible and robust MRS data fitting capabilities. Approach: PyAMARES imports prior knowledge from spreadsheets as initial values and constraints for fitting MRS data according to the AMARES model function. Results: PyAMARES effectively fits in vivo MRS data with varied algorithms, proving its versatility as a versatile fitting tool. Impact: PyAMARES provides accessible, flexible, and robust time-domain MRS fitting in Python, bridging the gap between advanced data analysis and deep learning by Python and the AMARES algorithm, previously limited to Java and MATLAB implementations.