Tutorials and Examples

Note

This section provides tutorials on how to use pyAMARES in Jupyter Notebooks. To start a Jupyter Notebook server from the command line, open a terminal and run:

jupyter notebook

Please see docs.jupyter.org for more details.

• File I/O Instruction
  • Read ASCII Data
  • Read GE MNS Research Pack fidall Data
  • Read NifTI-MRS Data
  • Read Binary jMRUI Data
  • Save MRS Data for pyAMARES Using Python
  • Save MRS Data for pyAMARES Using Matlab
• Prior Knowledge Spreadsheet for pyAMARES
  • Spreadsheet Format
  • Setting Up J-coupling Splitted Multiplets: An Example of In Vivo 31P MRS of the Human Brain at 7T
  • Use a Single Value in the Constraint Cell to Fix the Corresponding Parameter (New after version 0.3.4)
• Fitting Simulated In Vivo 31P MRS Data
  • Simulating an In Vivo MRS Spectrum
  • Simple Tutorial on pyAMARES Fitting
  • Visualize Fitting Results
• Examples of In Vivo X-Nuclei (\(^{129}\)Xe and \(^{2}\)H) MRS Fitting
  • Fitting a Voxel of Hyperpolarized \(^{129}\)Xe MRSI Acquired from Healthy Porcine Lungs at 3T
  • Fitting a Voxel of \(^{2}\)H 3D MRSI Acquired at 3T
• Fitting MRS with unknown species, using HSVDinitializer
• Frequency-Selective AMARES
  • Method 1: frequency-selective AMARES using MPFIR filter
  • Method 2: Frequency-Selective AMARES using Objective Function with frequency range
• Speed Up Batch Fitting Using Multiprocessing
  • Start AMARES fitting
• Using AMARES for Post-Processing: Removing Metabolite Residuals from Macromolecule (MM) Spectra
  • Two Methods of Analyzing 1H MRS Spectrum Using a Reference Peak (Water Peak)
  • Prior Knowledge of Residual Metabolites
  • AMARES Fitting (Method 1):
  • AMARES Fitting (Method 2):
  • Compare the Fitted Results
  • Compare to jMRUI Results: