python routines to analyze NEMA image quality phantom scans
Georg Schramm
This project is licensed under the MIT License - see the LICENSE file for details
We recommend to use the anaconda python distribution and to create a conda virtual environment for pynemaiqpet.
The installation consists of three steps:
- (optional) Installation of anaconda (miniconda) python distribution
- (optional) Creation of the conda virtual environment with all dependencies
- Installation of the pynemaiqpet package using pip
Although optional, we highly recommend to create and use a dedicated virtual conda python environment (steps 1 and 2).
Download and install Miniconda from https://docs.conda.io/en/latest/miniconda.html.
Please use the Python 3.x installer and confirm that the installer should run
conda init
at the end of the installtion process.
To create a virtual conda python=3.8 environment execute
conda create -n pynemaiqpet python=3.8 ipython
To test the installation of the virual environment, execute
conda activate pynemaiqpet
Activate the virtual conda environment
conda activate pynemaiqpet
Install pynemaiqpet package and all its dependecies
conda install -c gschramm -c conda-forge pynemaiqpet
To test the installation run (inside python or ipython)
import pynemaiqpet
print(pynemaiqpet.__version__)
print(pynemaiqpet.__file__) If the installation was successful, the command line tool pynemaiqpet_wb_nema_iq, which allows to automatically analyze WB NEMA IQ scans from the command line, should be installed.
To list see all its command line options and the help page run
pynemaiqpet_wb_nema_iq -h
To analyze the provided demo dicom data "pet_recon_2", you e.g. run:
pynemaiqpet_wb_nema_iq pet_recon_2 --fwhm_mm 5 --output_dir pet_recon_2_results --show --verbose
which will read all files in the direcory "pet_recon_2", post-smooth with Gaussian with FWHM = 5mm, show the output and finally save the output into the directory "pet_recon_2_results".