Fiber orientation estimation by constrained spherical deconvolution of the anisotropic edge illumination x-ray dark field signal

Ben Huyge; Ben Jeurissen; Jan De Beenhouwer; Jan Sijbers

doi:10.1117/12.2633482

14 October 2022 Fiber orientation estimation by constrained spherical deconvolution of the anisotropic edge illumination x-ray dark field signal

Ben Huyge, Ben Jeurissen, Jan De Beenhouwer, Jan Sijbers

Author Affiliations +

Proceedings Volume 12242, Developments in X-Ray Tomography XIV; 122420V (2022) https://doi.org/10.1117/12.2633482
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Abstract

Determining fiber orientations in a fiber reinforced material with X-ray CT is challenging because the width of the individual fibers is often much smaller than the spatial resolution of the X-ray images. However, the anisotropic X-ray scattering signal of the fibers yields information about the fiber direction without the need to individually resolve them. In this work, edge illumination phase contrast imaging is used to sample the anisotropic X-ray scattering in the object along different directions. The measured scatter is represented on a sphere by a set of spherical harmonics. Next, the orientation of the fibers is extracted from the scatter function by constrained spherical deconvolution. This approach is experimentally validated, for both parallel and crossing fiber distributions in a single voxel, using Monte Carlo simulations in GATE. The retrieved fiber orientations for parallel and crossing carbon fibers are presented as result.

Conference Presentation

Citation Download Citation

Ben Huyge, Ben Jeurissen, Jan De Beenhouwer, and Jan Sijbers "Fiber orientation estimation by constrained spherical deconvolution of the anisotropic edge illumination x-ray dark field signal", Proc. SPIE 12242, Developments in X-Ray Tomography XIV, 122420V (14 October 2022); https://doi.org/10.1117/12.2633482

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available