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.

翻译：数据驱动的深度学习已成功应用于各类计算机断层扫描重建问题。深度推理模型可能超越现有解析迭代算法，尤其在病态CT重建中表现优异。然而，这些方法预测的图像往往与测量投影数据不一致。本文提出了一种精确的可微前向与反向投影软件库，以确保预测图像与原始测量值之间的一致性。该软件库在最小化GPU内存占用需求的同时，高效支持多种投影几何类型，从而无缝集成到现有深度学习训练与推理流程中。所提出的软件已开源提供：https://github.com/LLNL/LEAP。