Features

(Almost) Full list of features for OMEGA v2.1.

Note

MATLAB/GNU Octave/Python refers that the features under that are available for all three, while some are available only for MATLAB/Octave (MATLAB/GNU Octave only) or Python (Python only). Furthermore, all features listed under Any data are available for any data type, i.e. PET, CT, SPECT or any other data, while those under PET Features are for PET only, CT features only for CT, etc.

MATLAB/GNU Octave/Python

Any data

  • OpenCL and CUDA support (single precision only)

  • Works with AMD, Nvidia or Intel GPUs

  • Has been tested with GPUs from all three (AMD in Windows, Intel in Linux, Nvidia in Linux)

  • Possibly also works with mobile device GPUs such as Qualcomm

  • 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

  • In addition to supporting coordinates for each measurement, supports also index-based reconstruction

  • Separate 16-bit index-vectors can be input for transaxial and axial dimensions

  • Each index corresponds to a coordinate in separate coordinate vectors (transaxial and axial)

  • Useful for symmetric systems where same coordinates (transaxial and/or axial) are repeated, such as cylindrical or “cubic” PET.

  • 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 your own 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, SART, ASD-POCS, FISTA, (A/E)COSEM, ROSEM, DRAMA, PDHG, CV, FDK/FBP, PDDY, SPS, RBI, SAGA, BB, and OSL

  • PDHG supports L1, L2, and Kullback-Leibler optimization

  • Optional FISTA/Momentum acceleration for all algorithms

  • 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, Lange, GGMRF, APLS, (proximal) TGV, proximal TV and hyperbolic prior

  • Several different non-local variations such as NLTV and NLRDP

  • TV, NL-methods and APLS support anatomic/prior image weighting

  • Supports time-varying dynamic data

  • Reconstruct dynamic data with static algorithms

  • 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

  • Select subsets optionally stochastically

  • 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/3D masks to limit forward projection and/or backprojection

  • 2D/3D mask in measurement space can be used to ignore certain measurements (values that are set at 0 are ignored)

  • Similarly in backprojection the 2D/3D 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 (tested with CT data only)

  • Allows the use of extended FOV without multi-resolution as well

  • Priors/regularization computed only in the main volume

  • Automatic cropping of the image

  • Dynamic reconstruction with static algorithms

  • Supports randoms/scatter smoothing

  • Supports pre-correcting the sinogram

PET features

  • Optimized for PET

  • Load GATE ROOT data for cylindrical/ECAT PET systems (GATE 9 and earlier)

  • 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

  • Reconstruct GATE 10 simulations in the same Python script

  • Load Inveon PET list-mode data

  • Automatically convert any of the above PET data into sinograms

  • 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 TOF with list-mode data

  • Supports pseudo detectors/rings or ring gaps

  • Supports easy inclusion of GATE attenuation maps as the attenuation correction images

  • Preliminary support for dual-layer PET

  • With index-based or listmode reconstruction, even multi-layer is possible

  • Supports arc correction for PET

  • Supports randoms variance reduction (PET only)

  • Supports increasing the sampling (i.e. interpolation) of PET sinograms

CT features

  • Optimized for (CB)CT 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 detector panel rotation in all three dimensions

  • Allows easy offset values for the source and/or detector location

  • 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

  • Optional log-based weighting

  • 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

  • Reconstruct even very large images with any GPU

  • Only a portion of the image and measurements are reconstructed simultaneously

  • Supports FDK, PDHG and PKMA

  • Regularizers supported: Non-local methods, RDP, GGMRF, TV, and hyperbolic prior

SPECT features

  • Optimized for parallel hole SPECT data

  • Load GATE SPECT projections images

  • Load Interfile SPECT projection images

  • Load Siemens Pro.specta DICOM data (requires additional toolboxes or packages)

  • Automatically compute detector response function for hexagonal or round holes

  • Supports rotation-based projector, orthogonal distance-based ray-tracer, and ray-based projector

  • Supports same subset selection methods as CT, though PET ones should work with the ray-based projector

  • Supports attenuation correction during reconstruction, either image-based or sinogram-based

  • Supports normalization correction during reconstruction

  • Supports easy inclusion of GATE attenuation maps as the attenuation correction images

MATLAB/GNU Octave only

  • Load GATE ASCII and LMF (LMF support has been deprecated) data for cylindrical/ECAT PET systems

  • 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, ROOT, 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 computation of the normalization coefficients from a normalization measurement (PET only)

  • Component-based

  • Supports sinogram gap filling

  • Supports scaling of CT-based attenuation coefficient to 511 keV attenuation coefficients

  • 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 (A is the class object)

  • x = A.T() * y computes the backprojection

  • Interoperability with PyOpenCL, Arrayfire OpenCL with PyOpenCL, CuPy, and PyTorch with CuPy

  • You can, for example, input a PyTorch CUDA tensor into OMEGA forward and/or backward projection

  • On OpenCL, you can use Arrayfire for fast GPU-based computations by simply inputting an Arrayfire array into forward and/or backward projection

  • Note that OMEGA is column-major while PyTorch is row-major!

  • Use Fortran-ordering with CuPy

  • Any package that supports PyOpenCL or CuPy can be combined with OMEGA

  • Supports standalone regularization functions

  • As with above, interoperability with PyOpenCL, Arrayfire OpenCL with PyOpenCL, CuPy, and PyTorch with CuPy

  • Compute RDP, GGMRF, TV (gradient-based), hyperbolic prior, or non-local regularization methods with any data

Features missing from Python

  • Subset type 10

  • Might be included in the future

  • ASCII data support for GATE

  • Probably won’t be included

  • Biograph support

  • Probably won’t be included

  • FMH and L-filter

  • Might be included in the future

  • Sinogram gap filling

  • Probably won’t be included

  • Automatic crop of voxelized phantoms/sources

  • Might be included in the future

  • CT attenuation coefficient scaling functions

  • Might be included in the future

  • Computation of the normalization coefficients from a normalization measurement

  • Will most likely be included in the future