OMEGA

Open-source multi-dimensional tomographic reconstruction software for MATLAB, GNU Octave and Python.

Purpose

The purpose of OMEGA is twofold. First it is designed to allow easy, fast and efficient reconstruction of any ray-tracing based tomographic imaging. Secondly, it is intended for easy algorithmic development as it allows easy matrix-free implementation of the forward (A * x) and backward (A^T * y) projections. While OMEGA allows the use of any ray-tracing based tomographic data, it is optimized for positron emission tomography (PET), computed tomography (CT) and single emission computed tomography (SPECT).

Introduction

OMEGA is a software for MATLAB, GNU Octave and Python to reconstruct tomographic data. See Features for more information on available features. See Known Issues and Limitations below for software limitations. If you wish to add your own code (e.g. reconstruction algorithm) see Contributing code to OMEGA.

Documentation for the current version is available at https://omega-doc.readthedocs.io/en/latest/index.html

The algorithms implemented so far are:

• Improved Siddon's ray tracer algorithm for the system matrix creation (code for regular Siddon available, but not used) [1,2]
• Orthogonal distance-based ray tracer [3]
• Volume of intersection ray tracer (THOR) [28].
• Interpolation-based ray tracer [31]
• Branchless distance-driven ray tracer [32,33]
• Rotation-based projector [34]
• Maximum Likelihood Expectation Maximization (MLEM) [4,5]
• Ordered Subsets Expectation Maximization (OSEM) [6]
• Complete-data Ordered Subsets Expectation Maximization (COSEM) [7]
• Enhanced COSEM (ECOSEM) [8]
• Accelerated COSEM (ACOSEM) [9]
• Row-Action Maximum Likelihood Algorithm (RAMLA) [10]
• Relaxed OSEM (ROSEM)
• Rescaled Block-Iterative EM (RBI) [11]
• Dynamic RAMLA (DRAMA) [12]
• Modified RAMLA (MRAMLA), aka modified BSREM-2 [13]
• Block Sequential Regularized Expectation Maximization (BSREM) [14]
• One-step-late algorithm (OSL) [15]
• Preconditioned Krasnoselskii-Mann algorithm (PKMA) [29]
• Primal-dual hybrid gradient [35]
• Condat-Vu [36,37]
• Primal-dual Davis-Yin [38]
• FISTA [39]
• FDK [40]
• LSQR [46]
• CGLS [47]
• SART [48,49]
• Quadratic prior (Gibbs prior with quadratic potential function)
• Huber prior [45]
• Median Root Prior (MRP) [16]
• L-filter (MRP-L) prior [17]
• Finite Impulse Response Median Hybrid (MRP-FMH) prior [17,18]
• Weighted mean prior [19,20]
• Total variation (TV) [21, 22, 23]
• Weighted TV [42]
• Total generalized variation (TGV) [24]
• Anisotropic diffusion (AD) Median Root Prior
• Asymmetric parallel levels sets prior (APLS) [22]
• Non-local means prior (NLM), including non-local TV [25,26,27]
• Relative difference prior [30]
• Generalized Gaussian Markov random field prior [41]
• Modified hyperbolic prior [23,43]
• Modified Lange prior [29,44]

Installation

For additional install help, see installation help.

Pre-built libraries are supplied in the releases, however, you can also manually compile everything.

For manual compilation you're going to need a C++ compiler in order to compile the MEX-files/libraries and use this software. Visual Studio and GCC have been tested to work and are recommended depending on your platform (Visual Studio on Windows, GCC on Linux, clang should work on MacOS). Specifically, Visual Studio 2019 and 2022 have been tested to work on Windows 10 and as well as g++ 9.3 and g++ 10.5 on Ubuntu 22.04. MinGW++ also works though it is unable to compile ArrayFire OpenCL reconstructions (implementation 2) on Windows by default. Octave supports only MinGW++ on Windows and as such implementation 2 on Windows is only supported if you manually compile ArrayFire from source with MinGW (for instructions, see here).

MinGW++ for MATLAB can be downloaded from here.

Visual studio can be downloaded from here. For Visual studio you'll only need "Desktop development with C++".

On Ubuntu you can install g++ with sudo apt install build-essential.

To install the OMEGA software, either simply extract the release/master package, download the MATLAB toolbox file from releases (OMEGA.-.Open-source.MATLAB.emission.tomography.software.mltbx) or obtain the source code through git:
git clone https://github.com/villekf/OMEGA and then add the OMEGA folder and subfolders to MATLAB/Octave path (this is done automatically if you install with the mltbx-file) or /path/to/OMEGA/source/Python to PYTHONPATH. Finally, run install_mex in the source folder to build the necessary MEX-files or compile.py with Python. ROOT, OpenCL and CUDA support will be installed, if the corresponding files are found. Possible compilation errors can be seen with install_mex(1). OpenCL include and library paths, ArrayFire path and ROOT path can also be set manually with install_mex(0, OpenCL_include_path, OpenCL_lib_path, AF_PATH, ROOT_PATH). OpenCL_include_path should be the folder where cl.h is located, OpenCL_lib_path the folder where OpenCL.lib/libOpenCL.so (Windows/Linux) is located, AF_PATH the path to ArrayFire installation location and ROOT_PATH to ROOT installation location. For Python the paths can be input with "python compile.py -R /path/to/ROOT -A /path/to/arrayfire -O /path/to/OpenCL".

Certain features on Octave (such as normalization calculation) require packages io and statistics. You can install them from the Octave user interface with the following commands (io has to be installed first):

pkg install -forge io

pkg install -forge statistics

and then you need to load the statistics package:

pkg load statistics

Python only requires NumPy, though to load mat-files you need pymatreader and for multi-resolution reconstruction scikit-image.

In order to enable OpenCL support (implementations 2 and 3), you're going to need an OpenCL SDK/library and (for implementation 2) ArrayFire (see below). On Linux you can alternatively just install the OpenCL headers and library. Below examples are for Ubuntu, but the packages should exist for other distros as well.

Headers (required only when manually building): sudo apt-get install opencl-headers

and then the library:
sudo apt-get install ocl-icd-opencl-dev

Alternative libraries in case the above one fails: sudo apt-get install nvidia-opencl-dev or sudo apt-get install intel-opencl-icd

In case the above doesn't work or you use Windows then you need to obtain an OpenCL SDK. The SDK can be any (or all) of the following: CUDA Toolkit, Intel OpenCL SDK, OCL-SDK, AMD APP SDK. On all cases, the OpenCL library and header files (only when manually building) need to be on your system's PATH. By default, the install_mex-file assumes that you have installed CUDA toolkit (Linux and Windows), AMD APP SDK v3.0 (Linux and Windows), OCL-SDK (Windows), AMD GPU Pro drivers (Linux) or Intel SDK (Linux and Windows). If you get an error message like "CL/cl.h: No such file or directory", the headers could not be found. You can manually add custom OpenCL paths with install_mex(0, '/path/to/cl.h', '/path/to/libOpenCL.so'). On Ubuntu you can use command find / -iname cl.h 2>/dev/null to find the required cl.h file and find / -iname libOpenCL.so 2>/dev/null to find the required library file. See install_mex.m for further details. compile.py functions similarly.

CUDA functionality requires CUDA toolkit.

All library paths needs to be on system path when running the mex-files or otherwise the required libraries will not be found.

Links:
https://software.intel.com/en-us/intel-opencl
https://developer.nvidia.com/cuda-toolkit
https://github.com/GPUOpen-LibrariesAndSDKs/OCL-SDK/releases

Once you have the header and library files, you need drivers/OpenCL runtimes for your device(s). If you have GPUs/APUs then simply having the vendor drivers should be enough. For Intel CPUs without an integrated GPU you need CPU runtimes (see the link below).

For AMD CPUs it seems that the AMD drivers released around the summer 2018 and after no longer support CPUs so you need an older driver in order to get CPU support or use an alternative runtime. One possibility is to use PoCL http://portablecl.org/ and another is to try the Intel runtimes (link below).

Intel runtimes can be found here: https://software.intel.com/en-us/articles/opencl-drivers

This software also uses ArrayFire library for the GPU/OpenCL implementation. You can find AF binaries from here:
https://arrayfire.com/binaries and the source code from here:
https://github.com/arrayfire/arrayfire

Installing/building ArrayFire to the default location (C:\Program Files\ArrayFire on Windows, /opt/arrayfire/ on Linux/MacOS) should cause install_mex and compile.py to automatically locate everything. However, in both cases you need to add the library paths to the system PATH. On Windows you will be prompted for this during the installation, for Linux you need to add /opt/arrayfire/lib or /opt/arrayfire/lib64 (depending which exists) to the library path (e.g. sudo ldconfig /opt/arrayfire/lib64/ or export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/arrayfire/lib64 if you don't have sudo permissions). Alternatively, on Linux, you can also build/install ArrayFire directly into the /usr/local/ folder.

Using CUDA code instead of OpenCL requires the CUDA toolkit. On both cases the CUDA folder should be on the system path. install_mex and compile.py always attempts to build the CUDA code as well so no additional input is required from the user if all the header and library data is found. By default install_mex and compile.py looks for CUDA in /usr/local/cuda/ on Linux. On Windows, CUDA location is determined from the environmental variables (PATH).

For additional install help, see installation help.

Getting Started

For detailed installation instructions, see https://omega-doc.readthedocs.io/en/latest/installation.html

Precompiled libraries are included in releases. However, in case those do not work you can also manually compile all the necessary files. When using MATLAB or GNU Octave, run install_mex first. For Python, you need run compile.py located in /path/to/OMEGA/source/Python.

For basic usage, see https://omega-doc.readthedocs.io/en/latest/usage.html

Examples for MATLAB/GNU Octave are in main-files folder, while for Python in the aforementioned Python-folder.

Features

See Features for more information on available features

Additional features

These features can be used as independent functions without any input needed from any other OMEGA files

• Save images (matrices) in MATLAB/Octave in NIfTI, MetaImage, Interfile, Analyze 7.5, DICOM and raw binary formats (saveImage.m)
• Import NIfTI, MetaImage, Interfile, Analyze 7.5, DICOM and raw binary formats into MATLAB/Octave (importData.m)
• Save images (matrices) in MATLAB/Octave in Interfile (saveInterfile.m) or MetaImage formats (saveMetaimage.m)
• Convert CT-attenuation coefficients into 511 keV attenuation coefficients (attenuationCT_to_511.m)
• (Experimental) Convert CT-attenuation coefficients directly from CT DICOM images into 511 keV attenuation coefficients (create_atten_matrix_CT.m)
• Convert COO (Coordinate list) sparse matrix row indices into CSR (Compressed sparse row) indices (coo_to_csr.m)
• Convert voxelized phantoms/sources into GATE compatible files (Voxelized_phantom_handle.m, Voxelized_source_handle.m)

System Requirements

MATLAB R2009a or later is mandatory. Following versions are guaranteed to work: 2022a and 2023b.

For Octave, any version above 5.0 should be fine. io, statistics and image packages are required for some features.

For Python 3.8 and above should work, though most likely earlier versions will work too.

C++11 compiler is required when manually compiling.

For Windows Visual Studio 2022 or 2019 is recommended with "Desktop development with C++", no other options are required. https://visualstudio.microsoft.com/

For Linux it is recommended to use g++ which usually comes bundled with the system.

On MacOS Xcode should be used https://apps.apple.com/us/app/xcode/id497799835?mt=12.

OpenCL library is required for OpenCL functionality.

ArrayFire is required for implementation 2.

For OpenCL, an OpenCL 1.2 compatible device is required. For CUDA, compute capability of 2.0 or higher is required.

The following third-party MATLAB codes are NOT required, but can be useful in certain specialized cases as they can be optionally used:
https://se.mathworks.com/matlabcentral/fileexchange/27076-shuffle (Shuffle, used by random subset sampling) https://se.mathworks.com/matlabcentral/fileexchange/22940-vol3d-v2 (vol3d v2, used for 3D visualization)
https://www.mathworks.com/matlabcentral/fileexchange/8797-tools-for-nifti-and-analyze-image (Tools for NIfTI and ANALYZE image, to load/save Analyze files and also NIfTI files in absence of image processing toolbox).

Known Issues and Limitations

MATLAB & Octave

LMF output currently has to contain the time stamp (cannot be removed in GATE) and detector indices. The source location needs to be included if it was selected in the main-file, same goes for the scatter data. If you have any other options selected in the LMF output in GATE, then you will not get any sensible detector data. Source locations and/or scatter data can be deselected. LMF data, with different format than in GATE, are not supported.

LMF source information is a lot more unreliable than the ASCII or ROOT version. LMF support has been deprecated in version 2.0.

Moving bed is not supported at the moment (needs to be step-and-shoot and the different bed positions need to be handled as separate cases).

Only cylindrical symmetric scanners are supported inherently, for other types of scanners the user has to input the detector coordinates or use index-based reconstruction.

For CT, only cone beam flat panel scanners are supported. For other types of scanners, the user has to input the detector coordinates or modify the data such that it is approximately flat panel.

ROOT or ASCII data is not yet supported with GATE CT data.

ECAT geometry is supported only with ASCII data. ROOT data might also work (untested).

If you are experiencing crashes when using implementation 2, it might be caused by the graphics features of ArrayFire. In this case I recommend renaming/removing the libforge.so files from the ArrayFire library folder (e.g. /opt/arrayfire/lib64/). Alternatively you can install the no-gl AF:
http://arrayfire.s3.amazonaws.com/index.html (3.6.2 is the latest). Finally, you can also simply build AF from source, preferably without building Forge. This seems to apply only to Linux and affects both MATLAB and Octave. Python is unaffected!

MATLAB

If you get GLIBCXX_3.4.XX/CXXABI_1.3.XX not found error (or similar with a different version number) or an error about "undefined reference to dlopen/dlclose/dlsomethingelse" when building or running files, this should be fixed with one of the methods presented here:
https://se.mathworks.com/matlabcentral/answers/329796-issue-with-libstdc-so-6

Or see the solutions in installation help.

If you are using ROOT data with ROOT 6.16.00 or newer you might receive the following error message: "undefined symbol: _ZN3tbb10interface78internal20isolate_within_arenaERNS1_13delegate_baseEl". This is caused by the libtbb.so.2 used by MATLAB (located in /matlabroot/bin/glnxa64). Same solutions apply as with the above case (e.g. renaming the file). See installation help for details.

ROOT data import is unstable in MATLAB R2018b and earlier versions due to a library incompatibility between the Java virtual machine in MATLAB and ROOT. On Linux you will experience MATLAB crashes when importing ROOT data. There is a workaround for this by using MATLAB in the no Java mode (e.g matlab -nojvm), though you won't have any GUI or graphic features. MATLAB R2019a and up are unaffected. It is recommended to use nojvm for data load only (set options.only_sinos = true to load only the data). The new desktop might not have this issue, but this is currently untested.

Octave

When using Windows implementation 2 (ArrayFire matrix free OpenCL) can only be enabled by manually building ArrayFire with Mingw. Instructions are provided here. Note that CUDA won't work even with manual building.

Almost all MATLAB-based code runs significantly slower compared to MATLAB (this is due to the slowness of loops in Octave). Reconstructions are unaffected.

MAT-files that are over 2 GB are not supported by Octave and such large data sets cannot be saved in Octave at the moment.

Python

Only implementation 2 is supported.

Status messages, such as the current iteration number, might be displayed only after the computation is already done.

Upcoming Features

Here is a list of features that should appear in future releases:

• New SPECT projector
• PET scatter correction based on SSS
• Improved dual-layer PET support

Reporting Bugs and Feature Requests

For any bug reports I recommend posting an issue on GitHub. For proper analysis I need the main-file that you have used and if you have used GATE data then also the macros. Preferably also all possible .mat files created, especially if the problem occurs in the reconstruction phase.

For feature requests, post an issue on GitHub. I do not guarantee that a specific feature will be added in the future.

Citations

If you wish to use this software in your work, cite this paper: V-V Wettenhovi et al 2021 Phys. Med. Biol. 66 065010. The peer reviewed (open access) paper on OMEGA can be found from https://doi.org/10.1088/1361-6560/abe65f.

If you use some of the specific algorithm or prior, please cite one of references here or some other original paper.

Acknowledgments

Original versions of COSEM, ACOSEM, ECOSEM, RAMLA, MRAMLA, MRP, L-filter, FMH, weighted mean, quadratic prior, sinogram coordinate and sinogram creation codes were written by Samuli Summala. Normalization coefficient and variance reduction codes were written by Anssi Manninen. Initial work on TOF was done by Jonna Kangasniemi. Initial work on SPECT was done by Matti Kortelainen and Akuroma George. All other codes were written by Ville-Veikko Wettenhovi. Some pieces of code were copied from various websites (Stack Overflow, MATLAB Answers), the original sources of these codes can be found in the source files.

This work was supported by a grant from Jane and Aatos Erkko foundation, Instrumentarium Science Foundation, Jenny and Antti Wihuri Foundation and The Finnish Research Impact Foundation. This work has been supported by University of Eastern Finland and Academy of Finland.

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