Features

Full list of features for OMEGA v2.0

MATLAB/GNU Octave/Python

Any data

• Supports any data that uses ray-tracing
  • Only the source and detector coordinates need to be input
  • Source and/or detector can be inside the FOV
• Supported projectors include:
  • Improved Siddon's ray tracer
    • Also multi-ray version available
  • Interpolation-based ray tracer
  • Volume of intersection ray tracer
  • Supports hybrid projectors
  • All projectors have 100% GPU support
• Use custom algorithms by using the built-in projectors
  • A class object can be created which can be used to compute the forward and/or backward projections
  • Available in MATLAB, GNU Octave and Python
• Wide range of algorithms supported:
• MLEM, OSEM, RAMLA, BSREM, MBSREM, PKMA, LSQR, CGLS, FISTA, (A/E)COSEM, ROSEM, DRAMA, PDHG, CV, FDK/FBP, PDDY, SPS, RBI and OSL
  • PDHG supports L1, L2 and Kullback-Leibler optimization
  • FDK/FBP supports several different windowing methods: Hamming, Hann, Blackman, Nuttal, Gaussian, Shepp-Logan, cosine, Parzen (de la Vallée Poussin) or none (ramp)
• Wide range of regularization techniques/priors:
• Quadratic prior, Huber, MRP, Weighted mean, TV, NLM, RDP, APLS, (proximal) TGV, proximal TV and hyperbolic prior
  • Several different non-local variations
  • TV, NLM and APLS support anatomic/prior image weighting
• Supports time-varying dynamic data
  • Reconstruct dynamic data with static algorithms
• OpenCL and CUDA support (single precision only)
• Point spread function blurring
  • Optional deblurring available
• Save the last iteration or specific iterations
• Supports subsets
  • Several different ways to select subsets
  • Non-PET/Non-CT/Non-SPECT data or list-mode PET data supports three subset selection methods
    • Divide the data into N segments
    • Take every Nth measurement
    • Randomly sample the measurement data
• Seven image-based preconditioners
  • Diagonal preconditioner
  • EM-preconditioner
  • IEM-preconditioner
  • Momentum-based preconditioner
  • Gradient-based preconditioner
  • Filtering-based preconditioner
  • Curvature-based preconditioner
• Two measurement-based preconditioners
  • Diagonal preconditioner
  • Filtering-based preconditioner
• Both filtering-based preconditioners support the same windowing functions as FDK/FBP
• Filtering-based preconditioner can optionally be used for N iterations/subiterations only
• Supports positivity enforcement for non-Poisson algorithms
• Supports manual initial values
• Allows the storage and output of the intermediate forward projections
• Insert scatter and/or randoms correction data into the reconstruction with supported algorithms (Poisson-based algorithms)
• Allows input of object offsets
  • If the object is not centered on the origin
• Use 2D masks to limit forward projection and/or backprojection
  • 2D mask in measurement space can be used to ignore certain measurements (values that are set at 0 are ignored)
  • Similarly in backprojection the 2D mask can be used to specify the voxels to reconstruct (likewise values that are 0 are not reconstructed)
• Supports multi-resolution reconstruction
  • Extended FOV can have reduced resolution
  • Resolution can be manually set
  • Can be set only for axial, only for transaxial or for both directions
  • Should work with all non-SPECT data
• Allows the use of extended FOV without multi-resolution as well
  • Priors/regularization computed only in the main volume
  • Automatic cropping of the image

PET features

• Optimized for PET
• Load GATE ROOT data for cylindrical/ECAT PET systems
  • Automatically convert the PET data into sinograms
  • Export trues, prompts, randoms and scatter sinograms
    • Rayleigh or Compton scatter in the detector and/or phantom can be separately selected
  • Form and reconstruct dynamic sinograms
  • Obtain a ground truth image from the GATE ROOT data
• Supports orthogonal distance-based ray tracer
• All projectors automatically use probabilities rather than the length of the line of intersection
• Automatically compute detector/source coordinates for cylindrical PET data (both GATE and non-GATE data)
• Several other subset selection methods
  • Use every Nth column sinogram bin
  • Use every Nth sinogram row
  • Use every Nth sinogram column
  • Use every Nth sinogram
  • Randomly sample the sinograms
  • Select the sinograms based on prime factors
• Supports attenuation correction during reconstruction, either image-based or sinogram-based
• Supports normalization correction during reconstruction
• Supports any manual sinogram-based correction
• Supports time-of-flight (TOF) data
• Supports formation of TOF sinograms from GATE data
• Supports list-mode data
• Supports pseudo detectors/rings or ring gaps
• Supports easy inclusion of GATE attenuation maps as the attenuation correction images

CT data

• Optimized for (CB)CT data
• Automatically load Planmeca CBCT data
• Automatically load image-based projections (e.g. tiff-images)
• Load GATE CT projections images
• Automatically compute source/detector coordinates for CBCT systems
  • Allows input of source and/or detector offsets
  • Supports multi-bed (step-and-shoot) data
• Supports GPU-optimized projectors
  • Voxel-based backprojector as well as the previously mentioned forward projectors
  • Branchless distance-driven projector, both for forward and backward projections
    • Allows subtraction of the DC-component
  • Supports hybrid projectors
• Supports projection image extrapolation
  • Automatically extrapolate and weight projections to fix out-of-FOV artifacts
• Supports offset correction
  • Offset weights can be automatically computed
  • Each projection has their own weight
• Several other subset selection methods
  • Use every Nth column of the projection image
  • Use every Nth projection image row
  • Use every Nth projection image column
  • Use every Nth projection image
  • Randomly sample the projection images
  • Select the projection images based on prime factors
• Most of the Poisson-based algorithms are supported with transmission-based (i.e. Lambert-Beer law) data as well
  • These include PKMA, MBSREM, RAMLA, ROSEM, OSEM, MLEM and BSREM

SPECT data

• Optimized for parallel hole SPECT data
• Load GATE SPECT projections images
• Automatically compute detector response function for hexagonal or round holes
• Supports rotation-based projector
• Supports same subset selection methods as CT

MATLAB/GNU Octave only

• Load GATE ASCII and LMF (LMF support has bee deprecated) data for cylindrical/ECAT PET systems
• Load Inveon PET list-mode data
• Load Siemens Biograph mCT and Vision list-mode data
  • Supports both binned 32-bit list-mode data as well as 64-bit
  • Supports also .ptd-files
• Automatically convert any of the above PET data into sinograms
• Obtain a ground truth image from GATE ASCII or LMF data (LMF support has bee deprecated)
• Several different "implementations" available that perform the computations either on the CPU or the GPU
  • Implementation 1 forms a sparse system matrix that is used in computations
    • Double precision only
    • System matrix can be extracted
    • System matrix can be created for only a subset of data
    • Supports all features except hyperbolic prior
  • Implementation 2 uses OpenCL or CUDA for the reconstructions
    • Supports all features
    • Single precision only
  • Implementation 3 uses OpenCL for the reconstructions
    • Supports only MLEM/OSEM
    • Single precision only
  • Implementation 4 is a parallel matrix-free CPU implementation
    • Uses OpenMP
    • Supports all features except hyperbolic prior
    • Single (default) or double precision
  • Implementation 5 is similar to implementation 4, except that forward and backward projections are performed using OpenCL
    • All other computations are done in MATLAB/GNU Octave
    • Supports all features except hyperbolic prior
    • Single precision only
• Supports custom algorithms with the use of OpenCL or CPU
  • A class object needs to be created first
  • Forward and/or backward projections are transferred to host (CPU) first if using OpenCL
  • Simply using y = A*x computes the forward projection when A is the class object
  • Similarly, x = A' * y computes the backprojection
  • Supports the system matrix approach, OpenCL or OpenMP (CPU)
  • For SPECT, only OpenMP version is available
• Visualization function that does not require any toolboxes
• Supports arc correction for PET (MATLAB only)
• Supports randoms/scatter smoothing
• Supports randoms variance reduction (PET only)
• Supports computation of the normalization coefficients from a normalization measurement (PET only)
  • Component-based
• Supports increasing the sampling (i.e. interpolation) of PET sinograms
• Supports sinogram gap filling
• Supports scaling of CT-based attenuation coefficient to 511 keV attenuation coefficients
• Supports pre-correcting the sinogram
• Allows to automatically crop voxelized phantoms/sources for MC simulations
• Individual functions to load MetaImage or Interfile data
• Few additional priors
  • FMH and L-filter

Python only

• Supports custom algorithms with the use of OpenCL or CUDA
  • All computations can be performed on the GPU without the need to transfer the data to host first
  • y = A * x computes the forward projection
  • x = A.T() * y computes the backprojection
  • Allows the seamless use of Arrayfire arrays for easy implementation of custom, user-made, algorithms without the need for GPU coding knowledge
  • Same code can be used for either OpenCL or CUDA