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 39 articles )

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  1. Andrade-Loarca, Héctor; Kutyniok, Gitta; Öktem, Ozan; Petersen, Philipp: Deep microlocal reconstruction for limited-angle tomography (2022)
  2. Shen, Ziju; Wang, Yufei; Wu, Dufan; Yang, Xu; Dong, Bin: Learning to scan: a deep reinforcement learning approach for personalized scanning in CT imaging (2022)
  3. Aramini, Riccardo; Delbary, Fabrice; Beltrametti, Mauro C.; Estatico, Claudio; Piana, Michele; Massone, Anna Maria: On the asymptotic equivalence between the Radon and the Hough transforms of digital images (2021)
  4. Bredies, Kristian; Huber, Richard: Convergence analysis of pixel-driven Radon and fanbeam transforms (2021)
  5. Brogaard Riis, Nicolai André; Dong, Yiqiu; Hansen, Per Christian: Computed tomography with view angle estimation using uncertainty quantification (2021)
  6. Bubba, Tatiana A.; Galinier, Mathilde; Lassas, Matti; Prato, Marco; Ratti, Luca; Siltanen, Samuli: Deep neural networks for inverse problems with pseudodifferential operators: an application to limited-angle tomography (2021)
  7. Celledoni, Elena; Ehrhardt, Matthias J.; Etmann, Christian; Owren, Brynjulf; Schönlieb, Carola-Bibiane; Sherry, Ferdia: Equivariant neural networks for inverse problems (2021)
  8. Chen, Chong: Spatiotemporal imaging with diffeomorphic optimal transportation (2021)
  9. Chouzenoux, Emilie; Pesquet, Jean-Christophe; Riddell, Cyril; Savanier, Marion; Trousset, Yves: Convergence of proximal gradient algorithm in the presence of adjoint mismatch (2021)
  10. Jerez, Andrés; Márquez, Miguel; Arguello, Henry: Adaptive coded aperture design for compressive computed tomography (2021)
  11. Kadu, Ajinkya; van Leeuwen, Tristan; Batenburg, K. Joost: CoShaRP: a convex program for single-shot tomographic shape sensing (2021)
  12. Kiefer, Lukas; Petra, Stefania; Storath, Martin; Weinmann, Andreas: Multi-channel Potts-based reconstruction for multi-spectral computed tomography (2021)
  13. Banert, Sebastian; Ringh, Axel; Adler, Jonas; Karlsson, Johan; Öktem, Ozan: Data-driven nonsmooth optimization (2020)
  14. 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)
  15. Cornelis, J.; Schenkels, N.; Vanroose, W.: Projected Newton method for noise constrained Tikhonov regularization (2020)
  16. Dong, Yiqiu; Schönlieb, Carola-Bibiane: Tomographic reconstruction with spatially varying parameter selection (2020)
  17. Lang, Lukas F.; Neumayer, Sebastian; Öktem, Ozan; Schönlieb, Carola-Bibiane: Template-based image reconstruction from sparse tomographic data (2020)
  18. Perelli, Alessandro; Lexa, Michael; Can, Ali; Davies, Mike E.: Compressive computed tomography reconstruction through denoising approximate message passing (2020)
  19. Toivanen, Jussi; Meaney, Alexander; Siltanen, Samuli; Kolehmainen, Ville: Joint reconstruction in low dose multi-energy CT (2020)
  20. Davide Micieli, Triestino Minniti, Giuseppe Gorini: NeuTomPy toolbox, a Python package for tomographic data processing and reconstruction (2019) not zbMATH

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