Core Imaging Library - Part II: multichannel reconstruction for dynamic and spectral tomography

Abstract

The newly developed core imaging library (CIL) is a flexible plug and play library for tomographic imaging with a specific focus on iterative reconstruction. CIL provides building blocks for tailored regularized reconstruction algorithms and explicitly supports multichannel tomographic data. In the first part of this two-part publication, we introduced the fundamentals of CIL. This paper focuses on applications of CIL for multichannel data, e.g. dynamic and spectral. We formalize different optimization problems for colour processing, dynamic and hyperspectral tomography and demonstrate CIL’s capabilities for designing state-of-the-art reconstruction methods through case studies and code snapshots. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 2’.

Crystalline phase discriminating neutron tomography using advanced reconstruction methods

“…A common way to solve the large-scale optimisation problem in equation ( 5) is by means of the fast iterative shrinkagethresholding algorithm (FISTA) [47] or the primal-dual hybrid gradient (PDHG) algorithm [48]. Here we used the implementation of both FISTA and PDHG in the CCPi CIL [27,28]. CIL provides a highly modular Python library for prototyping of reconstruction methods for multi-channel data such as spectral and dynamic (time-resolved) CT. CIL wraps a number of third-party libraries with hardware-accelerated building blocks for advanced tomographic algorithms.…”

“…TNV is a recent regulariser which enforces common edges across all channels in multi-channel images [24][25][26]. Wider implementation of these advanced reconstruction methods is possible through the open source CCPi Core Imaging Library (CIL) reconstruction framework [27,28], for example one may employ TV only, or TGV only, if desired. The performance of all methods is demonstrated on a multi-material sample comprising aluminium cylinders filled with various metallic powders of high purity.…”

Synergistic tomographic image reconstruction: part 2

“…The article by Cueva et al [11] proposes a new variant of directional TV regularization for spectral CT. Specifically, a spectral CT setup with three energy channels is considered and the three energy channel datasets are initially summed and reconstructed using TV-regularization [5] of hyperspectral X-ray tomographic imaging in three dimensions of gold and other mineral deposits using synergistic reconstruction methods. Localizing and distinguishing inclusions with similar densities is not possible using conventional X-ray CT. New energy-sensitive X-ray detectors can, in principle, distinguish inclusions through K-edge absorption profiles which act as elemental fingerprints.…”

“…Figure 1. Example from[5] of hyperspectral X-ray tomographic imaging in three dimensions of gold and other mineral deposits using synergistic reconstruction methods. Localizing and distinguishing inclusions with similar densities is not possible using conventional X-ray CT. New energy-sensitive X-ray detectors can, in principle, distinguish inclusions through K-edge absorption profiles which act as elemental fingerprints.…”

Core Imaging Library - Part I: a versatile Python framework for tomographic imaging

“…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 ].…”