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 optimisation framework for prototyping reconstruction methods including sparsity and total variation regularisation, as well as tools for loading, preprocessing and visualising 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.

中文翻译：

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核心成像库-第一部分：层析成像的通用Python框架

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，尤其着重于挑战性数据集的重建。对于例如在动态，光谱和原位层析成像中产生的高噪声，不完整，非标准或多通道数据，常规的滤波反投影重建往往不足。CIL为原型重建方法（包括稀疏性和总变异正则化）提供了广泛的模块化优化框架，以及用于加载，预处理和可视化层析成像数据的工具。CIL的功能在同步加速器示例数据集和三个具有挑战性的案例中得到了证明，这些案例涵盖了黄金比率中子断层扫描，锥束X射线断层扫描和正电子发射断层扫描。