Texture tomography with high angular resolution utilizing sparsity
Journal article, 2025

We demonstrate an approach to the reconstruction of scanning probe X-ray diffraction tomography data with anisotropic polycrystalline samples. The method involves reconstructing a voxel map containing an orientation distribution function in each voxel of a bulk 3D sample. By choosing a particular kind of basis functions, we can effectively utilize non-negativity in orientation space for samples with sparse texture. This enables us to achieve stable solutions at high angular resolutions where the problem would otherwise be underdetermined. This method differs from established approaches by not relying on a peak-finding step. It is therefore applicable to sample systems consisting of small and highly mosaic crystalline domains that are not handled well by these methods. We demonstrate the new approach using data from a shot-peened martensite sample where we are able to map the twinning microstructure in the interior of a bulk sample without resolving the individual lattice domains. We also demonstrate the approach on a piece of gastropod shell with a mosaic microstructure. The results suggest that, by utilizing the sparsity of the texture, the experiment can be carried out using only a single rotation axis, unlike previous demonstrations of texture and tensor tomography.

X-ray diffraction computed tomography

3D X-ray diffraction

tomography

XRDCT

3D-XRD

texture analysis

Author

Mads Carlsen

Paul Scherrer Institut

Florencia Malamud

Paul Scherrer Institut

Peter Modregger

University of Siegen

Deutsches Elektronen-Synchrotron (DESY)

Anna Wildeis

University of Siegen

Markus Hartmann

University of Siegen

Robert Brandt

University of Siegen

Andreas Menzel

Paul Scherrer Institut

Marianne Liebi

Paul Scherrer Institut

Swiss Federal Institute of Technology in Lausanne (EPFL)

Chalmers, Physics, Materials Physics

Journal of Applied Crystallography

0021-8898 (ISSN) 1600-5767 (eISSN)

Vol. 58 484-494

Subject Categories (SSIF 2025)

Materials Chemistry

DOI

10.1107/S1600576725001426

More information

Latest update

5/9/2025 7