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Tomographic imaging offers many challenges when it comes to sparse dynamic tomography. Due to the loss of information in the data and the ongoing motion, this problem calls for regularization. To reconstruct a moving 2D object, we propose a 3D variational formulation based on 3D shearlets, where the third-dimension accounts for the motion in time. Results on both simulated and real data show that better reconstructions are achieved when motion is considered.
This presentation is part of Minisymposium “MS33 - Advances in reconstruction algorithms for computed tomography (4 parts)”
organized by: Gunay Dogan (Theiss Research, NIST) , Harbir Antil (George Mason University) , Elena Loli Piccolomini (Dept. Computer Science and Engineering, University of Bologna) , Samuli Siltanen (University of Helsinki) .