Small-angle X-ray scattering tensor tomography: Model of the three-dimensional reciprocal-space map, reconstruction algorithm and angular sampling requirements
Journal article, 2018

Small-angle X-ray scattering tensor tomography, which allows reconstruction of the local three-dimensional reciprocal-space map within a three-dimensional sample as introduced by Liebi et al. [Nature (2015), 527, 349-352], is described in more detail with regard to the mathematical framework and the optimization algorithm. For the case of trabecular bone samples from vertebrae it is shown that the model of the three-dimensional reciprocal-space map using spherical harmonics can adequately describe the measured data. The method enables the determination of nanostructure orientation and degree of orientation as demonstrated previously in a single momentum transfer q range. This article presents a reconstruction of the complete reciprocal-space map for the case of bone over extended ranges of q. In addition, it is shown that uniform angular sampling and advanced regularization strategies help to reduce the amount of data required.The mathematical framework and reconstruction algorithm for small-angle scattering tensor tomography are introduced in detail, as well as strategies which help to reduce the amount of data and therewith the measurement time required. Experimental validation is provided for the application to trabecular bone.

small-angle X-ray scattering

bone

tensor tomography

spherical harmonics

Author

Marianne Liebi

Lund University

Chalmers, Physics, Condensed Matter Physics

Paul Scherrer Institut

Marios Georgiadis

Swiss Federal Institute of Technology in Zürich (ETH)

Joachim Kohlbrecher

Paul Scherrer Institut

M. Holler

Paul Scherrer Institut

Jörg Raabe

Paul Scherrer Institut

Ivan Usov

Paul Scherrer Institut

Andreas Menzel

Paul Scherrer Institut

Philipp Schneider

University of Southampton

Oliver Bunk

Paul Scherrer Institut

Manuel Guizar-Sicairos

Paul Scherrer Institut

Acta Crystallographica Section A: Foundations and Advances

2053-2733 (eISSN)

Vol. 74 1 12-24

Subject Categories

Computer Science

Computer Vision and Robotics (Autonomous Systems)

Medical Image Processing

DOI

10.1107/S205327331701614X

More information

Latest update

4/11/2018