Core Imaging Library -- Part II: Multichannel reconstruction for dynamic and spectral tomography
Not on the map. Why?
Abstract (arXiv)
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 regularised 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 formalise different optimisation 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.
Key facts. Cited by 9 papers in the map (data through 13 Oct 2026). First posted on arXiv 10 Feb 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 2; 2024: 4; 2025: 2; 2026: 1.
9 papers.
- 2024Unsupervised Multi-Parameter Inverse Solving for Reducing Ring Artifacts in 3D X-Ray CBCTcited by 4
- 2026Disentangled Learning Improves Implicit Neural Representations for Medical Reconstructioncited by 2
- 2025SPIDER: Structure-Preferential Implicit Deep Network for Biplanar X-ray Reconstructioncited by 1
- 2023Shot Noise Reduction in Radiographic and Tomographic Multi-Channel Imaging with Self-Supervised Deep Learningcited by 0
- 2023Single-shot Tomography of Discrete Dynamic Objectscited by 0
- 2024SAMCIRT: A Simultaneous Reconstruction and Affine Motion Compensation Technique for Four Dimensional Computed Tomography (4DCT)cited by 0
- 2024Stochastic Optimisation Framework using the Core Imaging Library and Synergistic Image Reconstruction Framework for PET Reconstructioncited by 0
- 2024Why do we regularise in every iteration for imaging inverse problems?cited by 0
- 2025An update to PYRO-NN: A Python Library for Differentiable CT Operatorscited by 0
- 2026Disentangled Learning Improves Implicit Neural Representations for Medical Reconstruction5 May 2026
- 2025An update to PYRO-NN: A Python Library for Differentiable CT Operators11 Nov 2025
- 2025SPIDER: Structure-Preferential Implicit Deep Network for Biplanar X-ray Reconstruction7 Jul 2025
- 2024Unsupervised Multi-Parameter Inverse Solving for Reducing Ring Artifacts in 3D X-Ray CBCT8 Dec 2024
- 2024Why do we regularise in every iteration for imaging inverse problems?1 Nov 2024
- 2024Stochastic Optimisation Framework using the Core Imaging Library and Synergistic Image Reconstruction Framework for PET Reconstruction21 Jun 2024
- 2024SAMCIRT: A Simultaneous Reconstruction and Affine Motion Compensation Technique for Four Dimensional Computed Tomography (4DCT)7 Feb 2024
- 2023Single-shot Tomography of Discrete Dynamic Objects9 Nov 2023
- 2023Shot Noise Reduction in Radiographic and Tomographic Multi-Channel Imaging with Self-Supervised Deep Learning25 Mar 2023