Computer Graphics
TU Braunschweig

Data-driven Compressed Sensing Tomography


Data-driven Compressed Sensing Tomography

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.

Reconstruction of measured data

Input measurement


 

Reconstructions

virtual view reconstruction
group sparsity reconstruction

Reconstruction of simulated data

Ground truth animation

Reconstruction with one input view

virtual view reconstruction
group sparsity reconstruction

Reconstruction with two input views

virtual view reconstruction
group sparsity reconstruction

 

Measurements and simulation data provided by Sandia National Laboratories

Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DENA0003525.


Author(s):Marc Kassubeck, Stephan Wenger, Chris A. Jennings, Matthew Gomez, Eric Harding, Jens Schwarz, Marcus Magnor
Published:January 2018
Type:Article in conference proceedings
Book:Electronic Imaging (Society for Imaging Science and Technology)
DOI:10.2352/ISSN.2470-1173.2018.15
Presented at:IS&T International Symposium on Electronic Imaging (EI) 2018


@inproceedings{kassubeck2018data-driven,
  title = {Data-driven Compressed Sensing Tomography},
  author = {Kassubeck, Marc and Wenger, Stephan and Jennings, Chris A. and Gomez, Matthew and Harding, Eric and Schwarz, Jens and Magnor, Marcus},
  booktitle = {Electronic Imaging},
  doi = {10.2352/{ISSN}.2470-1173.2018.15},
  volume = {2018},
  number = {15},
  month = {Jan},
  year = {2018}
}

Authors