  Data-driven deep learning has been successfully applied to various computed
tomographic reconstruction problems. The deep inference models may outperform
existing analytical and iterative algorithms, especially in ill-posed CT
reconstruction. However, those methods often predict images that do not agree
with the measured projection data. This paper presents an accurate
differentiable forward and back projection software library to ensure the
consistency between the predicted images and the original measurements. The
software library efficiently supports various projection geometry types while
minimizing the GPU memory footprint requirement, which facilitates seamless
integration with existing deep learning training and inference pipelines. The
proposed software is available as open source: https://github.com/LLNL/LEAP.
