PyTomography#

PyTomography is a python library for medical image reconstruction. It uses the functionality of PyTorch to (i) enable fast GPU-accelerated reconstruction and (ii) permit easy integration of deep-learning models in traditional reconstruction algorithms. If you use PyTomography in your own research, please cite the following: https://arxiv.org/abs/2309.01977

Features#

Modalities

Single Photon Computed Emission Tomography (SPECT)
- Support for attenuation correction, PSF modeling, and energy window based scatter correction
- Support for SIMIND and DICOM data, including stitching of multiple bed positions
Positron Emission Tomography (PET)
- Support for sinogram/listmode and time-of-flight reconstruction with attenuation, normalization, random, and scatter correction (time-of-flight based single scatter simulation)
- Support for GATE, HDF5, and PETSIRD data formats

Reconstruction Algorithms

Filtered Back Projection (FBP)
Statistical Iterative Algorithms
- Standard clinical algorithms such as OSEM / MLEM
- Regularized algorithms such as OSMAPOSL (see here) and BSREM (see here)
- AI-based algorithms such as DIPRecon (see here)

Installation#

Firstly, it is important that all your graphics card drivers are up to date. To ensure this, go to this link, enter your graphics card information, and download/install the newest possible driver. It is also recommended that you download anaconda using this link to manage your python environments.

Firstly, create a pytomography virtual environment using anaconda; the library requires python 3.11 or higher:

conda create --name pytomography_env -c conda-forge python=3.11

Then you can activate your environment, and install PyTomography:

conda activate pytomography_env
pip install pytomography

If you want to use the PET reconstruction options available, you need to additionally follow the installation instructions for parallelproj at this link.

Tutorials#

Be sure to check out Tutorials for some simple examples. If you wish to make a contribution, please read the Developer’s Guide.

User Forum#

If you have any questions about how to use the software, feel free to ask them on the discourse page at this link.

Examples#

Example 1: Reconstruction of Lu177-PSMA patient data using the available reconstruction algorithms.

Example 2: Ultra high resolution PET/MR brain phantom reconstructed using list-mode time-of-flight reconstruction with standard OSEM and the AI-Based Deep Image Prior reconstruction algorithm. Scatter estimation (listmode/time-of-flight) was obtained via single scatter simulation (SSS).