ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2021
Core Imaging Library - Part I: a versatile Python framework for tomographic imaging.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Quantitative Stain Mapping in X-Ray Virtual Histology.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Three-dimensional high-content imaging of unstained soft tissue with subcellular resolution using a laboratory-based X-ray microscope.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- PET rapid image reconstruction challenge (PETRIC).Frontiers in nuclear medicine · 2026Article
- HoToPy: a toolbox for X-ray holo-tomography in Python.Journal of synchrotron radiation · 2025Article
- Dynamic laboratory x-ray phase-contrast microtomography with structure-based prior regularisation.Measurement science & technology · 2025Article
- Unsupervised Denoising in Spectral CT: Multi-Dimensional U-Net for Energy Channel Regularisation.Sensors (Basel, Switzerland) · 2024Article
- tomoCAM: fast model-based iterative reconstruction via GPU acceleration and non-uniform fast Fourier transforms.Journal of synchrotron radiation · 2024Article
- Quantifying multiple stain distributions in bioimaging by hyperspectral X-ray tomography.Scientific reports · 2022Article
- Enhanced hyperspectral tomography for bioimaging by spatiospectral reconstruction.Scientific reports · 2021Article
- Synergistic tomographic image reconstruction: part 2.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021Article
- Core Imaging Library - Part I: a versatile Python framework for tomographic imaging.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021Article
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Authors and funding
11 authors.
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Abstract
We present the Core Imaging Library (CIL), an open-source Python framework for tomographic imaging with particular emphasis on reconstruction of challenging datasets. Conventional filtered back-projection reconstruction tends to be insufficient for highly noisy, incomplete, non-standard or multi-channel data arising for example in dynamic, spectral and
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