Abstract
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’.
Highlights
It is an exciting time for computed tomography (CT): existing imaging techniques are being pushed beyond current limits on resolution, speed and dose, while new ones are being continually developed [1]
Similar trends are seen across other imaging areas, including transmission electron microscopy (TEM), positron emission tomography (PET), magnetic resonance imaging (MRI) and neutron imaging, as well as joint or multi-contrast imaging combining several such modalities
Core Imaging Library (CIL) connects with other libraries to further combine and expand capabilities; we describe CIL plugins for ASTRA [6], TIGRE [7] and the Computational Project in Tomographic Imaging (CCPi)-Regularisation (CCPi-RGL) toolkit [16], as well as interoperability with the Synergistic Image Reconstruction Framework (SIRF) [17] enabling PET and MRI reconstruction using CIL
Summary
It is an exciting time for computed tomography (CT): existing imaging techniques are being pushed beyond current limits on resolution, speed and dose, while new ones are being continually developed [1]. Iterative reconstruction methods based on solving suitable optimization problems, such as sparsity and total variation (TV) regularization, have been applied with great success to improve reconstruction quality in challenging cases [3] This is highly specialized and time-consuming work that is rarely deployed for routine use. — Applied mathematicians and computational scientists can use existing mathematical building blocks and the modular design of CIL to rapidly implement and experiment with new reconstruction algorithms and compare them against existing state-of-the-art methods. They can run controlled simulation studies with test phantoms and within the same framework transition into demonstrations on real CT data. Multi-channel functionality (e.g. for dynamic and spectral CT) is presented in the part II paper [18] and a use case of CIL for PET/MR motion compensation is given in [19], both within this same issue; further applications of CIL in hyperspectral X-ray and neutron tomography are presented in [20,21]
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