PyAMARES, an Open-Source Python Library for Fitting Magnetic Resonance Spectroscopy Data
Note
PyAMARES is currently in its early stages of development and is actively being improved. We welcome any suggestions or reports of issues you may encounter. Feel free to contact us with your feedback on GitHub or at jia-xu-1@uiowa.edu.
What is pyAMARES?
PyAMARES package to provide the MRS community with 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.
PyAMARES fits MRS data to the AMARES model function :
\[y_n = \hat{y}_n + e_n = \sum_{k=1}^{K} a_k e^{j\phi_k} e^{-d_k (1-g_k+g_k t_n) t_n} e^{j2\pi f_k t_n} + e_n, \quad n = 0,1,...,N - 1.\]
The parameters \(a_k\) (amplitude), \(f_k\) (frequency), \(d_k\) (damping factor), \(\phi_k\) (phase), and \(g_k\) (lineshape) can be fitted or fixed by pyAMARES as needed.
- Getting Started - A Simple Example
- What is pyAMARES?
- Why use pyAMARES?
- Installation Guide
- Tutorials and Examples
- File I/O Instruction
- Prior Knowledge Spreadsheet for pyAMARES
- Fitting Simulated In Vivo 31P MRS Data
- Examples of In Vivo X-Nuclei (\(^{129}\)Xe and \(^{2}\)H) MRS Fitting
- Fitting MRS with unknown species, using HSVDinitializer
- Frequency-Selective AMARES
- Speed Up Batch Fitting Using Multiprocessing
- Using AMARES for Post-Processing: Removing Metabolite Residuals from Macromolecule (MM) Spectra
- Code Reference
- License
- How to Cite
- Contributing
- Changelog
- About Us