Welcome to OMEGA documentation!

OMEGA, or open-source multi-dimensional tomographic reconstruction software, is an image reconstruction toolkit for tomographic imaging, optimized for positron emission tomography (PET), single photon emission computed tomography (SPECT) and computed tomography (CT). However, any ray-tracing based tomographic imaging is supported. The software works on MATLAB, GNU Octave and Python. For a full list of features see Features.

See Installation for install help. Check out the Usage section for further information.

Note

This project is under active development.

Note

This documentation is for OMEGA v.2.1 and up. Separate documentation for v.2.0 is available from the menu in the lower right corner. For versions v.0.9-1.2, see https://github.com/villekf/OMEGA/wiki

Contents

• Installation
  • MATLAB/Octave
  • Python
  • Windows
    • MATLAB
    • Octave
    • Python
      • pip install
      • Manual install
      • Other notes
    • C++ Compiler
    • OpenCL/CUDA
    • ArrayFire
    • OMEGA
      • Python pip install
      • Other cases
        • MATLAB/Octave
        • Python
  • Linux
    • MATLAB
    • Octave
    • Python
      • pip install
      • Manual install
      • Other notes
    • C++ Compiler
    • OpenCL/CUDA
    • ArrayFire
    • OMEGA
      • Python pip install
      • Other cases
        • MATLAB/Octave
        • Python
  • Mac
    • MATLAB
    • Octave
    • Python
      • pip install
      • Manual install
      • Other notes
    • C++ Compiler
    • OpenCL/CUDA
    • ArrayFire
    • OMEGA
      • Python pip install
      • Other cases
        • MATLAB/Octave
        • Python
  • Optional software
• Examples
  • Installation
  • General Information
  • PET data
  • CT data
  • SPECT data
  • Contact
• Usage
  • Geometry information
    • PET data
    • CT data
    • SPECT data
    • Other data
  • Verbosity
  • Adjustable parameters
    • PET scanner variables
    • CT scanner variables
    • SPECT scanner variables
    • Projector settings
    • Image volume settings
    • Sinogram settings
    • Computing settings
    • TOF settings
    • Correction settings
    • GATE specific settings
    • Reconstruction specific settings
    • Preconditioner settings
    • Dynamic imaging settings
    • Misc. settings
    • Algorithm settings
    • Regularization settings
• OMEGA geometry
  • PET geometry
    • Transaxial
    • Axial
  • CT/SPECT geometry
    • Transaxial
    • Axial
    • Fan beam CT data
    • Parallel beam CT data
    • Helical CT data
  • Other data
• Selecting the optimal projector
  • PET data
    • PSF modeling
  • CT data
  • SPECT data
  • Other data
• Choosing the optimal implementation
• Image reconstruction
  • Built-in reconstruction
  • Using the forward and/or backward projection operators
  • Standalone regularization
  • Creating and storing the system matrix
• OpenCL vs. CUDA vs. CPU
• Algorithms
  • Subset types
    • Type 0
    • Type 1
    • Type 2
    • Type 3
    • Type 4
    • Type 5
    • Type 6
    • Type 7
    • Type 8
    • Type 9
    • Type 10
    • Type 11
    • Stochastic subset selection
  • Reconstruction algorithms
    • FDK/FBP
    • MLEM/OSEM
    • RAMLA
    • MRAMLA
    • ROSEM
    • RBI
    • DRAMA
    • COSEM
    • ECOSEM
    • ACOSEM
    • FISTA
    • FISTAL1
    • LSQR
    • CGLS
    • SART
    • OSL-OSEM
    • MBSREM
    • BSREM
    • ROSEM-MAP
    • OSL-RBI
    • OSL-COSEM
    • PKMA
    • PDHG
    • CV
    • PDHGL1
    • PDHGKL
    • PDDY
    • ASD-POCS
    • SAGA
    • FISTA acceleration
  • Priors
    • Quadratic
    • Huber prior
    • MRP
    • L-filter
    • FMH
    • Weighted mean
    • TV
    • Proximal TV
    • Anisotropic Diffusion Median Root Prior
    • APLS
    • Hyperbolic prior
    • TGV
    • RDP
    • GGMRF
    • NLM
• Preconditioners
  • Image-based preconditioners
    • Diagonal preconditioner
    • EM preconditioner
    • IEM preconditioner
    • Momentum-like preconditioner
    • Gradient-based preconditioner
    • Filtering-based preconditioner
    • Curvature-based preconditioner
  • Measurement-based preconditioners
    • Diagonal preconditioner
    • Filtering-based preconditioner
• Data/artifact corrections
  • Arc correction
  • Randoms correction
  • Scatter correction
  • Attenuation correction
  • Normalization correction
  • Out-of-FOV correction
    • Multi-resolution
  • Offset correction
  • Resolution recovery
• Using your own source/detector coordinates or list-mode data
  • Index-based reconstruction
  • Coordinate-based reconstruction
  • Using list-mode data
  • TOF-data
• High-dimensional computing
  • High-dimensional measurement data
  • High-dimensional CT/image data
• Loading GATE data
  • GATE 9 and earlier PET data
    • Coincidence mask
  • Extracting GATE scatter, randoms and trues data from GATE 9 data
    • Usage
    • Using ASCII data
    • Using ROOT data
    • Loading and saving data
    • Reconstruction
  • Using GATE 9/10 SPECT or CT data
  • GATE 10 PET data
• Using mask images
  • Masks in forward projection
  • Masks in backprojection
• Features
  • MATLAB/GNU Octave/Python
    • Any data
    • PET features
    • CT features
    • SPECT features
  • MATLAB/GNU Octave only
  • Python only
  • Features missing from Python
• Using TOF data
  • TOF properties
  • Enabling TOF
  • Inputing custom TOF values
  • TOF in other PET data
  • TOF integration points
• Technical info
  • Data load
    • Execution
      • GATE data
      • List-mode data
      • Sinogram data
      • Saving data
  • Forming sinograms
    • Execution
  • Attenuation correction
  • Normalization correction
  • Image reconstruction
    • Implementation 1
    • Implementation 2
    • Implementation 3
    • Implementation 4
    • Matrix-free formulation
    • TOF coefficients
• Useful links
  • Simulation software
  • Reconstruction software
  • Data analysis software
  • Programming and languages
  • Deep learning based denoisers
  • Datasets
• Modifying OMEGA source code
  • Adding built-in algorithms
  • Debugging