| Abstract: |
| The Core Imaging Library (CIL) is an open-source Python framework for solving imaging inverse problems, with a particular emphasis on tomography modalities such as X-ray CT, MRI, and PET. CIL supports multiple stages of the imaging workflow, including data reading, preprocessing, reconstruction and visualisation, making it a versatile platform for developing and testing advanced imaging methods. A central strength of CIL is its modular optimisation framework, which enables users to combine operators, data fidelity terms, and regularisation functionals to formulate and solve a wide range of smooth and non-smooth optimisation problems using first-order methods. Recent developments further extend these capabilities through a flexible stochastic optimisation framework, allowing researchers to configure and test different algorithmic building blocks, sampling strategies, and step-size rules. In this talk, I will introduce CIL, outline its role across the imaging pipeline, and highlight in particular its optimisation module, together with examples from real-world imaging applications that demonstrate its value for developing, testing, and benchmarking advanced methods for inverse problems. |
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