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
Install miniconda or miniforge
Install the miniconda or miniforge (conda from conda-forge) python distribution and to be able create a conda virtual environment for pyapetnet.
conda create -n pyapetnet python~=3.10 pip
Installation of a stable version of pyapetnet from pypi
conda activate pyapetnet
pip install pyapetnet
Install the latest (unstable) version (not recommended)
conda activate pyapetnet
cd /foo/bar/my_favorite_dir
git clone git@github.com:gschramm/pyapetnet.git
cd pyapetnet
pip install .
Testing the installation
To test the installation activate the virtual conda environment
conda activate pyapetnet
and run
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 pyapetnet subfolder here and here.
Training your own model
If you want to train your own model (from scratch or using transfer learning) using your own data, have a look at our training script. All input parameters (e.g. data sets to use) have to be specified in a config json file (example here). The input data sets have to be in nifti format and should be aligned already.