pyAMARES, an Open-Source Python Library for Fitting Magnetic Resonance Spectroscopy Data

The full documentation for pyAMARES can be found at pyAMARES Documentation.

What is pyAMARES?

The pyAMARES package provides the MRS community with an open-source, easy-to-use MRS fitting method in Python. It imports prior knowledge from Excel or CSV spreadsheets as initial values and constraints for fitting MRS data according to the AMARES model function.

Getting Started

Requirements

Note

PyAMARES requires Python 3.6 or newer. We recommend using Python 3.8 or newer. If you are using an older version of Python, you will need to upgrade to use pyAMARES.

Installation

pip install pyAMARES

See the Installation Guide for detailed information.

Run pyAMARES as standard-alone script

amaresFit -f ./pyAMARES/examples/fid.txt -p  ./pyAMARES/examples/example_human_brain_31P_7T.csv --MHz 120.0 --sw 10000 --deadtime 300e-6 --ifplot --xlim 10 -20 -o simple_example 

Run pyAMARES in a Jupyter Notebook

Try Jupyter Notebook on Google Colab here

import pyAMARES
# Load FID from a 2-column ASCII file, and set the MR parameters
MHz = 120.0 # 31P nuclei at 7T
sw = 10000 # spectrum width in Hz
deadtime = 300e-6 # 300 us begin time for the FID signal acquisition

fid = pyAMARES.readmrs('./pyAMARES/examples/fid.txt')
# Load Prior Knowledge
FIDobj = pyAMARES.initialize_FID(fid=fid, 
                                 priorknowledgefile='./pyAMARES/examples/example_human_brain_31P_7T.csv',
                                 MHz=MHz, 
                                 sw=sw,
                                 deadtime=deadtime, 
                                 preview=False, 
                                 normalize_fid=False,
                                 xlim=(10, -20))# Region of Interest for visualization, -20 to 10 ppm

# Initialize the parameter using Levenberg-Marquard method
out1 = pyAMARES.fitAMARES(fid_parameters=FIDobj,
                           fitting_parameters=FIDobj.initialParams,
                           method='leastsq',
                           ifplot=False)

# Fitting the MRS data using the optimized parameter

out2 = pyAMARES.fitAMARES(fid_parameters=out1,
                          fitting_parameters=out1.fittedParams, # optimized parameter for last step
                          method='least_squares',
                          ifplot=False)

# Save the data
out2.styled_df.to_html('simple_example.html') # Save highlighted table to an HTML page
                                              # Python 3.6 does not support to_html. 
out2.result_sum.to_csv('simple_example.csv') # Save table to CSV spreadsheet
out2.plotParameters.lb = 2.0 # Line Broadening factor for visualization
out2.plotParameters.ifphase = True # Phase the spectrum for visualization
pyAMARES.plotAMARES(fid_parameters=out1, filename='simple_example.svg') # Save plot to SVG 

Fitting Result for Example 31P MRS data

How to cite

If you use pyAMARES in your research, please consider citing the following ISMRM proceeding:

Jia Xu, Rolf F. Schulte, Baolian Yang, Michael Vaeggemose, Christoffer Laustsen, and Vincent A. Magnotta, Proc. Intl. Soc. Mag. Reson. Med. 32 (2024) 2996.

This citation is based on the current conference proceedings and is tentative. A journal paper is expected to be published in the future, and users will be encouraged to cite the formal publication once it is available.