pynemaiqpet 0.5.4

Analysis of PET NEMA IQ phantom scans

pynemaiqpet

python routines to analyze NEMA image quality phantom scans

Authors

Georg Schramm

License

This project is licensed under the MIT License - see the LICENSE file for details

Installation

We recommend to use the anaconda python distribution and to create a conda virtual environment for pynemaiqpet.

The installation consists of three steps:

1. (optional) Installation of anaconda (miniconda) python distribution
2. (optional) Creation of the conda virtual environment with all dependencies
3. 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).

Installation of anaconda (miniconda)

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.

Creation of the virtual conda environment

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

Installation of the pynemaiqpet package

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__) 

Run demos

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".