Feasibility Study of a Track-Based Multiple Scattering Tomography
We propose a new tomographic imaging method based on the multiple Coulomb scattering processes of electrons in the GeV-range. Particle tracking devices are used for a position-resolved measurement of the distribution of scattering angles at a sample. From this, an estimate of the two-dimensional material budget distribution of the sample is extracted. Repeating this measurement for different particle incidence angles enables a reconstruction of the three-dimensional material budget distribution.
To demonstrate the feasibility of this technique, simulations have been performed including the underlying physical processes of charged particles traversing matter, as well as the electronic response of the tracking detectors. We compare two data reconstruction models and discuss the simulation results in terms of the reconstructed image contrast.
We appreciate the support of C. Kleinwort regarding discussions about the GBL formalism and its development.
- 2.Benoit, M., Idarraga, J., Arfaoui, S.: AllPix Detector Simulation Framework. https://github.com/ALLPix/allpix. Accessed 24 June 2017
- 4.Diener, R., Meyners, N., Potylitsina-Kube, N., Stanitzki, M.: Test Beams at DESY. http://testbeam.desy.de. Accessed 26 July 2016
- 6.Jansen, H.: Resolution studies with the datura beam telescope. J. Inst. 11(12), C12031 (2016)Google Scholar
- 9.Jansen, H., Dreyling-Eschweiler, J., Schütze, P., Spannagel, S.: Scattering studies with the DATURA beam telescope. In These Proceedings, TIPP (2017)Google Scholar
- 10.Bulgheroni, A., et al.: EUTelescope, the JRA1 Tracking and Reconstruction Software: A Status Report (Milestone), Technical report (2008). Accessed 21 Apr 2015Google Scholar
- 11.EUTelescope Software Developers. EUTelescope Website. http://eutelescope.desy.de. Accessed 21 Apr 2015
- 12.Blobel, V., Kleinwort, C., Meier, F.: Fast alignment of a complex tracking detector using advanced track models. Comput. Phys. Commun. 182(9), 1760–1763 (2011). Computer Physics Communications Special Edition for Conference on Computational Physics, Trondheim, Norway, 23–26 June 2010ADSCrossRefGoogle Scholar
- 16.Scikit-image Development Team. scikit-image - Image Processing in Python. http://scikit-image.org/. Accessed 24 June 2017
- 17.van der Walt, S., Schönberger, J.L., Nunez-Iglesias, J., Boulogne, F., Warner, J.D., Yager, N., Gouillart, E., Yu, T., and the scikit-image contributors: Scikit-Image: Image processing in Python. Peer J., 2, e453 (2014)Google Scholar