pyapetnet 1.1.1

a CNN for anatomy-guided deconvolution and denoising of PET images

pyapetnet

A convolutional neurol network (CNN) to mimick the behavior of anatomy-guided PET reconstruction in image space.

Authors

Georg Schramm, David Rigie

License

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

Scientific Publication

Details about pyapetnet are published in Schramm et al., "Approximating anatomically-guided PET reconstruction in image space using a convolutional neural network" ,NeuroImage Vol 224 2021. If we you are using pyapetnet in scientific publications, we appreciate citation of this article.

Installation

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

The installation consists of three steps:

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

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.

To test your miniconda installtion, open a new terminal and execute

conda list

which should list the installed basic python packages.

Creation of the virtual conda environment

To create a virtual conda python=3.8 environment execute

conda create -n pyapetnet python=3.8 ipython

You can also you a newer version of python, if supported by tensorflow. To test the installation of the virual environment, execute

conda activate pyapetnet

Installation of the pyapetnet package

Activate the virual conda environment

conda activate pyapetnet

The easiest is to install pyapetnet simply from the python package index via

pip install pyapetnet

which will install the pyapetnet package inside the virtual conda environment.

To test the installation run (inside python or ipython)

import pyapetnet
print(pyapetnet.__version__)
print(pyapetnet.__file__) 

If the installation was successful, a number of command line scripts all starting with pyapetnet* to e.g. do predictions with the included trained models from nifti and dicom input images will be available.

Getting started - running your first prediction with pre-trained models

To run a prediction using one of included pre-trained networks and nifti images, run e.g.:

pyapetnet_predict_from_nifti osem.nii t1.nii S2_osem_b10_fdg_pe2i --show

Use the following to get information on the (optional) input arguments

pyapetnet_predict_from_nifti -h

To get a list of available pre-trained models run

pyapetnet_list_models

To make predictions from dicom images, use

pyapetnet_predict_from_dicom osem_dcm_dir t1_dcm_dir S2_osem_b10_fdg_pe2i --show

The source code of the prediction scripts can be found in the command_line_tools sub module.