We present the Core Imaging Library (CIL), an open-source Python framework for tomographic imaging with particular emphasis on reconstruction of challenging datasets. Conventional filtered back-projection reconstruction tends to be insufficient for highly noisy, incomplete, non-standard or multi-channel data arising for example in dynamic, spectral and in situ tomography. CIL provides an extensive modular optimization framework for prototyping reconstruction methods including sparsity and total variation regularization, as well as tools for loading, preprocessing and visualizing tomographic data. The capabilities of CIL are demonstrated on a synchrotron example dataset and three challenging cases spanning golden-ratio neutron tomography, cone-beam X-ray laminography and positron emission tomography.

This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 2’.

我们介绍了核心成像库 (CIL)，这是一个用于断层成像的开源 Python 框架，特别强调具有挑战性的数据集的重建。对于例如在动态、光谱和原位断层扫描中出现的高噪声、不完整、非标准或多通道数据，传统的滤波反投影重建往往是不够的。CIL 为原型重建方法提供了广泛的模块化优化框架，包括稀疏性和总变异正则化，以及用于加载、预处理和可视化断层扫描数据的工具。CIL 的功能在同步加速器示例数据集和跨越黄金比例中子断层扫描、锥形束 X 射线层析成像和正电子发射断层扫描的三个具有挑战性的案例中得到了展示。

本文是主题问题“协同断层图像重建：第 2 部分”的一部分。