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Volume: 30 | Article ID: art00008
Data-driven Compressed Sensing Tomography
  DOI :  10.2352/ISSN.2470-1173.2018.15.COIMG-133  Published OnlineJanuary 2018

This paper presents a new method for tomographic reconstruction of volumes from sparse observational data. Application scenarios can be found in astrophysics, plasma physics, or whenever the amount of obtainable measurement is limited. In the extreme only a single view of the phenomenon may be available. Our method uses input image data together with complex, user-definable assumptions about 3D density distributions. The parameter values of the user-defined model are fitted to the input image. This allows for incorporating complex, data-driven assumptions, such as helical symmetry, into the reconstruction process. We present two different sparsity-based reconstruction approaches. For the first method, novel virtual views are generated prior to tomography reconstruction. In the second method, voxel groups of similar target densities are defined and used for group sparsity reconstruction. We evaluate our method on real data of a high-energy plasma experiment and show that the reconstruction is consistent with the available measurement and 3D density assumptions. An additional experiment on simulated data demonstrates possible gains when adding an additional view to the presented reconstruction methods.

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Marc Kassubeck, Stephan Wenger, Chris A. Jennings, Matthew Gomez, Eric Harding, Jens Schwarz, Marcus Magnor, "Data-driven Compressed Sensing Tomographyin Proc. IS&T Int’l. Symp. on Electronic Imaging: Computational Imaging XVI,  2018,  pp 133-1 - 1336,

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