Open AccessRegularized iterative integration combined with non-linear diffusion filtering for phase-contrast x-ray computed tomography
Author(s) -
Karin Burger,
Thomas Kœhler,
Michael Chabior,
Sebastian Allner,
Mathias Marschner,
Andreas Fehringer,
Marian Willner,
Franz Pfeiffer,
Peter B. Noël
Publication year - 2014
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.22.032107
Subject(s) - phase contrast imaging , contrast (vision) , optics , contrast to noise ratio , tomography , phase retrieval , kernel (algebra) , algorithm , mathematics , materials science , computer science , image quality , physics , artificial intelligence , fourier transform , mathematical analysis , phase contrast microscopy , image (mathematics) , combinatorics
Phase-contrast x-ray computed tomography has a high potential to become clinically implemented because of its complementarity to conventional absorption-contrast.In this study, we investigate noise-reducing but resolution-preserving analytical reconstruction methods to improve differential phase-contrast imaging. We apply the non-linear Perona-Malik filter on phase-contrast data prior or post filtered backprojected reconstruction. Secondly, the Hilbert kernel is replaced by regularized iterative integration followed by ramp filtered backprojection as used for absorption-contrast imaging. Combining the Perona-Malik filter with this integration algorithm allows to successfully reveal relevant sample features, quantitatively confirmed by significantly increased structural similarity indices and contrast-to-noise ratios. With this concept, phase-contrast imaging can be performed at considerably lower dose.