The ASTRA Tomography Toolbox is a MATLAB toolbox based on high-performance GPU primitives for 2D and 3D tomography, developed jointly by the ASTRA-Vision Lab research group at the University of Antwerp and CWI, Amsterdam. It supports 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 to enable building new algorithms.

References in zbMATH (referenced in 30 articles )

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  1. Bredies, Kristian; Huber, Richard: Convergence analysis of pixel-driven Radon and fanbeam transforms (2021)
  2. Brogaard Riis, Nicolai André; Dong, Yiqiu; Hansen, Per Christian: Computed tomography with view angle estimation using uncertainty quantification (2021)
  3. Chouzenoux, Emilie; Pesquet, Jean-Christophe; Riddell, Cyril; Savanier, Marion; Trousset, Yves: Convergence of proximal gradient algorithm in the presence of adjoint mismatch (2021)
  4. Jerez, Andrés; Márquez, Miguel; Arguello, Henry: Adaptive coded aperture design for compressive computed tomography (2021)
  5. Kiefer, Lukas; Petra, Stefania; Storath, Martin; Weinmann, Andreas: Multi-channel Potts-based reconstruction for multi-spectral computed tomography (2021)
  6. Banert, Sebastian; Ringh, Axel; Adler, Jonas; Karlsson, Johan; Öktem, Ozan: Data-driven nonsmooth optimization (2020)
  7. Bubba, Tatiana A.; Heikkilä, Tommi; Help, Hanna; Huotari, Simo; Salmon, Yann; Siltanen, Samuli: Sparse dynamic tomography: a shearlet-based approach for iodine perfusion in plant stems (2020)
  8. Lang, Lukas F.; Neumayer, Sebastian; Öktem, Ozan; Schönlieb, Carola-Bibiane: Template-based image reconstruction from sparse tomographic data (2020)
  9. Perelli, Alessandro; Lexa, Michael; Can, Ali; Davies, Mike E.: Compressive computed tomography reconstruction through denoising approximate message passing (2020)
  10. Toivanen, Jussi; Meaney, Alexander; Siltanen, Samuli; Kolehmainen, Ville: Joint reconstruction in low dose multi-energy CT (2020)
  11. Davide Micieli, Triestino Minniti, Giuseppe Gorini: NeuTomPy toolbox, a Python package for tomographic data processing and reconstruction (2019) not zbMATH
  12. Dong, Yiqiu; Hansen, Per Christian; Hochstenbach, Michiel E.; Brogaard Riis, Nicolai André: Fixing nonconvergence of algebraic iterative reconstruction with an unmatched backprojector (2019)
  13. Guo, Yan; Aveyard, Richard; Rieger, Bernd: A multichannel cross-modal fusion framework for electron tomography (2019)
  14. Kazantsev D, Pasca E, Turner MJ, Withers PJ: CCPi-Regularisation toolkit for computed tomographic image reconstruction with proximal splitting algorithms (2019) not zbMATH
  15. Marquez, Miguel; Arguello, Henry: Coded aperture optimization for single pixel compressive computed tomography (2019)
  16. Matteo Ravasi, Ivan Vasconcelos: PyLops - A Linear-Operator Python Library for large scale optimization (2019) arXiv
  17. Neumayer, Sebastian; Persch, Johannes; Steidl, Gabriele: Regularization of inverse problems via time discrete geodesics in image spaces (2019)
  18. Sindre Nordmark Olufsen: AXITOM: A Python package for reconstruction of axisymmetric tomograms acquired by a conical beam (2019) not zbMATH
  19. Soubies, Emmanuel; Soulez, Ferréol; McCann, Michael T.; Pham, Thanh-an; Donati, Laurène; Debarre, Thomas; Sage, Daniel; Unser, Michael: Pocket guide to solve inverse problems with GlobalBioim (2019)
  20. Chambolle, Antonin; Ehrhardt, Matthias J.; Richtárik, Peter; Schönlieb, Carola-Bibiane: Stochastic primal-dual hybrid gradient algorithm with arbitrary sampling and imaging applications (2018)

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