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  • Introduction
  • Framework
  • Read/ write AcquisitionData and ImageData
  • Optimisation framework
  • Processors
  • Recon
  • Utilities
  • CIL Plugins
  • Developers’ Guide
    • Tutorials
    • User showcase
  • Introduction
  • Framework
  • Read/ write AcquisitionData and ImageData
  • Optimisation framework
  • Processors
  • Recon
  • Utilities
  • CIL Plugins
  • Developers’ Guide
  • Tutorials
  • User showcase
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Table of Contents

  • Introduction
  • Framework
  • Read/ write AcquisitionData and ImageData
  • Optimisation framework
  • Block Framework
  • Processors
  • Recon
  • Utilities
  • CIL Plugins
  • Developers’ Guide
  • Tutorials
  • User showcase
    • Multibang Regularisation in CIL
    • Showcase of the algorithms for deblurring and denoising
    • 1D inverse problem demo using deriv2 from regtools
    • TV-regularized reconstruction of the dynamix STEMPO dataset in CIL
    • Dynamic MR Reconstruction
    • a data reader for CIL
    • Aims of this session
    • Main steps
    • Create directories
    • Import packages
    • Step 1. Download and extract the phantom data from a ZIP file.
    • Step 2. Extract surface meshes from the voxelied phantom.
    • Step 3. Simulate an X-ray radiograph of the virtual patient.
    • Step 4. Select the number of incident photons per pixel
    • Step 5. Add the corresponding amount of Photonic noise
    • Step 6. Create the flat-field images with the corresponding amount of Photonic noise.
    • Step 7. Simulate a CT scan
    • Step 8. Reconstruct the CT volume using the Core Imaging Library (CIL)
    • Cleaning up
    • Reconstruction and regularisation for a hyperspectral dataset
    • Comparison between the Least Square and the Weighted Least Square and the Kullback Leibler Divergence
    • Reconstructing the noisy data using KL divergence with TV regularisation
    • Compare all the reconstructions
    • Comparisons between LS, WLS and KL loss for a range of counts
    • Offset reconstruction
    • Introduction
    • Set variables and paths
    • Create acquisition geometry
    • Read files
    • Data processing
    • Prepare back-projection
    • Review projection data before running the recontruction
    • Perform reconstruction
    • Display results
    • Optional: Save results as tiff files
    • Optional: Save results as json files
    • Exciscope Polaris phase contrast reconstruction with CIL
    • Controlled Wavelet Sparsity using callbacks (2D data)
    • Wavelet based regularization
    • Controlled Wavelet Sparsity using callbacks (3D data)
    • Wavelet based regularization
    • CIL User Showcase 13: Anisotropic Regularization for FILD Measurements using CIL
    • Simulation using gVXR and CPU reconstruction using CIL of a twisted hexagonal object with helical flow channels
    • Read projections
    • Reconstruction
    • Conclusion
    • Memory and Performance Profiling CIL Algorithms: CGLS vs. LSQR
    • A CIL-pytorch example using DeepInverse using a pre-trained denoiser in the CIL FISTA algorithm
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  • User showcase

User showcase#

This page contains example notebooks demonstrating how CIL users have solved a variety of interesting problems using CIL tools. Click below to see the rendered notebooks, or access and run the code yourself from https://github.com/TomographicImaging/CIL-User-Showcase/tree/main

Note: each notebook has been tested on a specified version of CIL and is not guaranteed to run on the latest CIL release

Multibang Regularisation in CIL
Showcase of the algorithms for deblurring and denoising
1D inverse problem demo using deriv2 from regtools
TV-regularized reconstruction of the dynamix STEMPO dataset in CIL
Dynamic MR Reconstruction
a data reader for CIL
Reconstruction and regularisation for a hyperspectral dataset
Comparison between the Least Square and the Weighted Least Square and the Kullback Leibler Divergence
Offset reconstruction
Introduction
Exciscope Polaris phase contrast reconstruction with CIL
Controlled Wavelet Sparsity using callbacks (2D data)
Controlled Wavelet Sparsity using callbacks (3D data)
CIL User Showcase 13: Anisotropic Regularization for FILD Measurements using CIL
Simulation using gVXR and CPU reconstruction using CIL of a twisted hexagonal object with helical flow channels
Memory and Performance Profiling CIL Algorithms: CGLS vs. LSQR
A CIL-pytorch example using DeepInverse using a pre-trained denoiser in the CIL FISTA algorithm

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CIL Callbacks How To

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Multibang Regularisation in CIL

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