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On the Characteristics of Helical 3D X-Ray Dark-Field Imaging

  • Lina FelsnerEmail author
  • Shiyang Hu
  • Veronika Ludwig
  • Gisela Anton
  • Andreas Maier
  • Christian Riess
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

The X-ray dark-field can be measured with a grating interferometer. For oriented structures like fibers, the signal magnitude depends on the relative orientation between fiber and gratings. This allows to analytically reconstruct the fiber orientations at a micrometer scale. However, there currently exists no implementation of a clinically feasible trajectory for recovering the full 3D orientation of a fiber. In principle, a helical trajectory can be suitable for this task. However, as a first step towards dark-field imaging in a helix, a careful analysis of the signal formation is required. Towards this goal, we study in this paper the impact of the grating orientation. We use a recently proposed 3D-projection model and show that the projected dark-field scattering at a single volume point depends on the grating sensitivity direction and the helix geometry. More specifically, the dark-field signal on a 3D trajectory always consists of a linear combination of a constant and an angular-dependent component.

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Literatur

  1. 1.
    Jensen TH, Bech M, Bunk O, et al. Directional X-ray dark-field imaging. Phys Med Biol. 2010;55(12):3317.CrossRefGoogle Scholar
  2. 2.
    Revol V, Kottler C, Kaufmann R, et al. Orientation-selective X-ray dark field imaging of ordered systems. J Appl Phys. 2012;112(11):114903.CrossRefGoogle Scholar
  3. 3.
    Scherer K, Yaroshenko A, Bölükbas DA, et al. X-ray dark-field radiography-in-vivo diagnosis of lung cancer in mice. Sci Rep. 2017;7(1):402.Google Scholar
  4. 4.
    Hellbach K, Baehr A, Marco F, et al. Depiction of pneumothoraces in a large animal model using X-ray dark-field radiography. Sci Rep. 2018;8(1):2602.Google Scholar
  5. 5.
    Wieczorek M, Schaff F, Jud C, et al. Brain connectivity exposed by anisotropic X-ray dark-field tomography. Sci Rep. 2018;8.Google Scholar
  6. 6.
    Bayer FL, Hu S, Maier A, et al. Reconstruction of scalar and vectorial components in X-ray dark-field tomography. Procs Nat Acad Sci. 2014;111(35):12699{12704.CrossRefGoogle Scholar
  7. 7.
    Hu S, Riess C, Hornegger J, et al. 3D tensor reconstruction in X-ray dark-field tomography. Proc BVM. 2015; p. 492–497.Google Scholar
  8. 8.
    Malecki A, Potdevin G, Biernath T, et al. X-ray tensor tomography. Europhys Lett. 2014;105(3):38002.CrossRefGoogle Scholar
  9. 9.
    Vogel J, Schaff F, Fehringer A, et al. Constrained X-ray tensor tomography reconstruction. Optics Express. 2015;23(12):15134{15151.CrossRefGoogle Scholar
  10. 10.
    Wieczorek M, Schaff F, Pfeiffer F, et al. Anisotropic X-ray dark-field tomography: a continuous model and its discretization. Phys Rev Lett. 2016;117(15):158101.Google Scholar
  11. 11.
    Schaff F, Prade F, Sharma Y, et al. Non-iterative directional dark-field tomography. Sci Rep. 2017;7(1):3307.Google Scholar
  12. 12.
    Hu S, Felsner L, Maier A, et al. A 3-D projection model for X-ray dark-field imaging. arXiv:181104457. 2018;.

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019

Authors and Affiliations

  • Lina Felsner
    • 1
    Email author
  • Shiyang Hu
    • 1
  • Veronika Ludwig
    • 2
  • Gisela Anton
    • 2
  • Andreas Maier
    • 1
  • Christian Riess
    • 1
  1. 1.Pattern Recognition Lab, Computer ScienceUniv. of Erlangen-NürnbergErlangenDeutschland
  2. 2.Erlangen Centre for Astroparticle PhysicsUniv. of Erlangen-NürnbergErlangenDeutschland

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