  There is a need for open-source libraries in emission tomography that (i) use
modern and popular backend code to encourage community contributions and (ii)
offer support for the multitude of reconstruction algorithms available in
recent literature, such as those that employ artificial intelligence. The
purpose of this research was to create and evaluate a GPU-accelerated,
open-source, and user-friendly image reconstruction library, designed to serve
as a central platform for the development, validation, and deployment of
various tomographic reconstruction algorithms. PyTomography was developed using
Python and inherits the GPU-accelerated functionality of PyTorch and
parallelproj for fast computations. Its flexible and modular design decouples
system matrices, likelihoods, and reconstruction algorithms, simplifying the
process of integrating new imaging modalities using various python tools.
Example use cases demonstrate the software capabilities in parallel hole SPECT
and listmode PET imaging. Overall, we have developed and publicly share
PyTomography, a highly optimized and user-friendly software for medical image
reconstruction, with a class hierarchy that fosters the development of novel
imaging applications.
