The ASTRA Toolbox is a MATLAB and Python toolbox of high-performance GPU primitives for 2D and 3D tomography.
We support 2D parallel and fan beam geometries, and 3D parallel and cone beam. All of them have highly flexible source/detector positioning.
A large number of 2D and 3D algorithms are available, including FBP, SIRT, SART, CGLS.
The basic forward and backward projection operations are GPU-accelerated, and directly callable from MATLAB and Python to enable building new algorithms.
See the MATLAB and Python code samples in samples/ directory and on http://www.astra-toolbox.com/.
Requirements: conda Python environment, with 64 bit Python 3.9-3.12.
Conda packages for ASTRA Toolbox are available from the astra-toolbox channel, whereas CUDA-related packages can be installed from nvidia channel.
To install ASTRA into the desired conda environment, run:
conda install astra-toolbox -c astra-toolbox -c nvidia
We also provide development packages:
conda install astra-toolbox -c astra-toolbox/label/dev -c nvidia
Add the mex and tools subdirectories to your MATLAB path, or install the Python wheel using pip. We require the Microsoft Visual Studio 2017 redistributable package. If this is not already installed on your system, it is included as vc_redist.x64.exe in the ASTRA zip file.
Requirements: g++ (7 or higher), CUDA (11.0 or higher), MATLAB (R2012a or higher)
cd build/linux
./autogen.sh # when building a git version
./configure --with-cuda=/usr/local/cuda \
--with-matlab=/usr/local/MATLAB/R2012a \
--prefix=$HOME/astra \
--with-install-type=module
make
make install
Add $HOME/astra/matlab and its subdirectories (tools, mex) to your MATLAB path.
If you want to build the Octave interface instead of the MATLAB interface,
specify --enable-octave instead of --with-matlab=.... The Octave files
will be installed into $HOME/astra/octave . On some Linux distributions
building the Astra Octave interface will require the Octave development package
to be installed (e.g., liboctave-dev on Ubuntu).
NB: Each MATLAB version only supports a specific range of g++ versions.
Despite this, if you have a newer g++ and if you get errors related to missing
GLIBCXX_3.4.xx symbols, it is often possible to work around this requirement
by deleting the version of libstdc++ supplied by MATLAB in
MATLAB_PATH/bin/glnx86 or MATLAB_PATH/bin/glnxa64 (at your own risk),
or setting LD_PRELOAD=/usr/lib64/libstdc++.so.6 (or similar) when starting
MATLAB.
Requirements: g++ (7 or higher), CUDA (11.0 or higher), Python (3.x), Cython, six, scipy
cd build/linux
./autogen.sh # when building a git version
./configure --with-cuda=/usr/local/cuda \
--with-python \
--with-install-type=module
make
make install
This will install Astra into your current Python environment.
Requirements: g++ (7 or higher), CUDA (11.0 or higher)
cd build/linux
./autogen.sh # when building a git version
./configure --with-cuda=/usr/local/cuda
make
make install-dev
This will install the Astra library and C++ headers.
Use the Homebrew package manager to install boost, libtool, autoconf, automake.
cd build/linux
./autogen.sh
CPPFLAGS="-I/usr/local/include" NVCCFLAGS="-I/usr/local/include" ./configure \
--with-cuda=/usr/local/cuda \
--with-matlab=/Applications/MATLAB_R2016b.app \
--prefix=$HOME/astra \
--with-install-type=module
make
make install
Requirements: Visual Studio 2017 (full or community), boost (recent), CUDA (11.0 or higher), MATLAB (R2012a or higher) and/or WinPython 3.x.
Using the Visual Studio IDE:
Set the environment variable MATLAB_ROOT to your MATLAB install location. Copy boost headers to lib\include\boost, and boost libraries to lib\x64. Open build\msvc\astra_vc14.sln in Visual Studio. Select the appropriate solution configuration (typically Release_CUDA|x64). Build the solution. Install by copying AstraCuda64.dll and all .mexw64 files from build\msvc\bin\x64\Release_CUDA and the entire matlab/tools directory to a directory to be added to your MATLAB path.
Using .bat scripts in build\msvc:
Edit build_env.bat and set up the correct directories. Run build_setup.bat to automatically copy the boost headers and libraries. For MATLAB: Run build_matlab.bat. The .dll and .mexw64 files will be in bin\x64\Release_Cuda. For Python 3.12: Run build_python312.bat. Astra will be directly installed into site-packages.
To perform a (very) basic test of your ASTRA installation in Python, you can run the following Python command.
import astra
astra.test()
To test your ASTRA installation in MATLAB, the equivalent command is:
astra_test
If you use the ASTRA Toolbox for your research, we would appreciate it if you would refer to the following papers:
W. van Aarle, W. J. Palenstijn, J. Cant, E. Janssens, F. Bleichrodt, A. Dabravolski, J. De Beenhouwer, K. J. Batenburg, and J. Sijbers, “Fast and Flexible X-ray Tomography Using the ASTRA Toolbox”, Optics Express, 24(22), 25129-25147, (2016), http://dx.doi.org/10.1364/OE.24.025129
W. van Aarle, W. J. Palenstijn, J. De Beenhouwer, T. Altantzis, S. Bals, K. J. Batenburg, and J. Sijbers, “The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography”, Ultramicroscopy, 157, 35–47, (2015), http://dx.doi.org/10.1016/j.ultramic.2015.05.002
Additionally, if you use parallel beam GPU code, we would appreciate it if you would refer to the following paper:
W. J. Palenstijn, K J. Batenburg, and J. Sijbers, "Performance improvements for iterative electron tomography reconstruction using graphics processing units (GPUs)", Journal of Structural Biology, vol. 176, issue 2, pp. 250-253, 2011, http://dx.doi.org/10.1016/j.jsb.2011.07.017
The ASTRA Toolbox is open source under the GPLv3 license.
email: [email protected] website: https://www.astra-toolbox.com/
Copyright: 2010-2024, imec Vision Lab, University of Antwerp 2014-2024, CWI, Amsterdam https://visielab.uantwerpen.be/ and https://www.cwi.nl/