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

arXiv: https://arxiv.org/abs/2102.06126 · HTML: https://mlsanity.com/paper/2102.06126/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2021.

## Built on by (most cited first, 9 of 9)

- [Unsupervised Multi-Parameter Inverse Solving for Reducing Ring Artifacts in 3D X-Ray CBCT](https://mlsanity.com/paper/2412.05853/) (2024)
- [Disentangled Learning Improves Implicit Neural Representations for Medical Reconstruction](https://mlsanity.com/paper/2605.04234/) (2026)
- [SPIDER: Structure-Preferential Implicit Deep Network for Biplanar X-ray Reconstruction](https://mlsanity.com/paper/2507.04684/) (2025)
- [Shot Noise Reduction in Radiographic and Tomographic Multi-Channel Imaging with Self-Supervised Deep Learning](https://mlsanity.com/paper/2303.14429/) (2023)
- [Single-shot Tomography of Discrete Dynamic Objects](https://mlsanity.com/paper/2311.05269/) (2023)
- [SAMCIRT: A Simultaneous Reconstruction and Affine Motion Compensation Technique for Four Dimensional Computed Tomography (4DCT)](https://mlsanity.com/paper/2402.04480/) (2024)
- [Stochastic Optimisation Framework using the Core Imaging Library and Synergistic Image Reconstruction Framework for PET Reconstruction](https://mlsanity.com/paper/2406.15159/) (2024)
- [Why do we regularise in every iteration for imaging inverse problems?](https://mlsanity.com/paper/2411.00688/) (2024)
- [An update to PYRO-NN: A Python Library for Differentiable CT Operators](https://mlsanity.com/paper/2511.08427/) (2025)

## 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.