A convolutional neurol network (CNN) to mimick the behavior of anatomy-guided PET reconstruction in image space.
Georg Schramm, David Rigie
This project is licensed under the MIT License - see the LICENSE file for details.
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.
We recommend to install pyapetnet from conda-forge via
conda -c conda-forge install pyapetnet
Alternatively, pyapetnet can be also installed from pypi via
pip install pyapetnet
To test the installation activate your virtual environment and run
import pyapetnet
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.
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 scripts subfolder here.
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.