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: g++, CUDA (10.2 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.
-- This has not been tested for nonrigid package --
Requirements: g++, CUDA (10.2 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 $HOME/astra/octave . On some Linux distributions
building the Astra Octave interface will require the Octave development package
to be installed (e.g., liboctave-dev on Ubuntu).
NB: Each MATLAB version only supports a specific range of g++ versions.
Despite this, if you have a newer g++ and if you get errors related to missing
GLIBCXX_3.4.xx symbols, it is often possible to work around this requirement
by deleting the version of libstdc++ supplied by MATLAB in
MATLAB_PATH/bin/glnx86 or MATLAB_PATH/bin/glnxa64 (at your own risk),
or setting LD_PRELOAD=/usr/lib64/libstdc++.so.6 (or similar) when starting
MATLAB.
-- This has not been tested for nonrigid package --
Requirements: g++, CUDA (10.2 or higher)
cd build/linux
./autogen.sh # when building a git version
./configure --with-cuda=/usr/local/cuda
make
make install-dev
This will install the Astra library and C++ headers.
-- This has not been tested for nonrigid package --
Use the Homebrew package manager to install boost, libtool, autoconf, automake.
cd build/linux
./autogen.sh
CPPFLAGS="-I/usr/local/include" NVCCFLAGS="-I/usr/local/include" ./configure \
--with-cuda=/usr/local/cuda \
--with-matlab=/Applications/MATLAB_R2016b.app \
--prefix=$HOME/astra \
--with-install-type=module
make
make install
-- This has not been tested for nonrigid package --
Requirements: Visual Studio 2017 (full or community), boost (recent), CUDA (10.2 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 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 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
If you use the nonrigid computed tomography code, we would appreciate it if you would refer to the following paper:
M. Odstrcil, M. Holler, J. Raabe, A. Sepe, X. Sheng, S. Vignolini, C. G. Schroer, and M. Guizar-Sicairos. "Ab initio nonrigid X-ray nanotomography." Nature communications 10, no. 1 (2019): 2600, https://doi.org/10.1038/s41467-019-10670-7
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/