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’.
Highlights
Over recent years in X-ray computed tomography (CT), there has been a growing interest in dynamic and spectral CT thanks to the technological advancements on detector speed and sensitivity and on multichannel photon-counting detectors (PCDs), as depicted in the EPSRC Tomography roadmap [1].In dynamic CT, the aim is to reconstruct a series of images and depict the complete spatiotemporal response of the scanned object
The scope of this paper is to present the capabilities of the Core Imaging Library (CIL), releases available at [4,5], of the collaborative computational project in tomographic imaging (CCPi) for multichannel tomography
We show the pre- and post-scan filtered back projection (FBP) reconstructions acting as the reference images, along with |ξv|2 that illustrates how edge information is captured by ξv to be included by the directional total variation (dTV)
Summary
Over recent years in X-ray computed tomography (CT), there has been a growing interest in dynamic and spectral CT thanks to the technological advancements on detector speed and sensitivity and on multichannel photon-counting detectors (PCDs), as depicted in the EPSRC Tomography roadmap [1]. The aim is to solve the total variation (TV) denoizing problem using the fast gradient projection (FGP) algorithm [8]. The cil.optimization framework contains three structures, namely Function, Operator and Algorithm that formalize a generic optimization problem for imaging applications as n−1 u∗ = arg min f (Ku) + g(u) ≡ arg min fiKi(u) + g(u). The functions f , g allow us to define a fidelity term, that measures the distance between the acquired data b and the forward-projected reconstruction image as well as a regularizer, which enforces a certain regularity on u. In order to find an approximate solution for minimization problems of the (2.1) form, we use a different CIL Algorithm for smooth and non-smooth objective functions such as the conjugate gradient least squares (CGLS), simultaneous iterative reconstruction technique (SIRT).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
