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X-Ray Computed Tomography


This work aims at developing high quality and robust 3D reconstructions from X-ray 2D and 3D cone beam CT scanners in the presence of noise and using sparse views. 
  • We utilize the flexible numerical optimization framework of proximal algorithms like the ADMM method. 
  • We developed a high quality solver based on the SART algorithm and combined it with several regularizers that achieved better results that state-of-the-art. 
  • We extended a structure tensor-based regularizer to 3D and applied it to specific structures such as thin sheets and fibers.
  • We also developed solvers for different noise models, and advanced structure tensor-based regularizers for handling special structures in the scanned objects. 
  • We compared different proximal solvers for the tomography problem, including SART, ART, BICAV, and Ordered Subsets. 
  • We developed solvers for the Gaussian and Poisson noises. 
  • We performed extensive experiments on 2D synthetic and real datasets.


  • TRex toolbox for X-Ray CT Reconstruction.


  • Guangming Zang, Mohamed Aly, Peter Wonka, and Wolfgang Heidrich. Super-resolution CT Reconstruction. In preparation, 2017.
  • Mohamed Aly, Guangming Zang, Wolfgang Heidrich, and Peter Wonka. TRex: A Tomography Reconstruction Proximal Framework for Robust Sparse View X-Ray Applications. arXiv preprint arXiv:1606.03601, 2016, [pdf]


This is joint work with Guangming Zang, Wolfgang Heidrich, and Peter Wonka.