Spatially variant optimization of an empirical beam-hardening correction algorithm

Andriy Andreyev; Faguo Yang; Matthew Andrew; Lars Omlor; Herminso Villarraga-Gómez

doi:10.1117/12.2632254

10 November 2022 Spatially variant optimization of an empirical beam-hardening correction algorithm

Andriy Andreyev, Faguo Yang, Matthew Andrew, Lars Omlor, Herminso Villarraga-Gómez

Author Affiliations +

Proceedings Volume 12242, Developments in X-Ray Tomography XIV; 122421G (2022) https://doi.org/10.1117/12.2632254
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Abstract

In this work we consider a well-known empirical beam-hardening correction algorithm applied to metal artifact reduction in cone beam micro-computed tomography (CT). In its basic form, this algorithm consists of a few simple steps: segmentation of metal components out of uncorrected image data, forward projection to create metal only projection data, and the creation of several correction basis images. The set of basis images can then be combined to create a corrected image with mitigated metal artifacts. The combination weights can be determined either manually or automatically by solving an optimization equation, be performed globally or locally (allowing weights to vary spatially). The latter approach may be more attractive as it may account for larger local variances in scatter artifacts, however, not practical for manual optimization, requiring an automated approach. We apply both global and spatially variant version of the algorithm to datasets from a cone-beam micro-CT (using a 3D X-ray microscope) and study the performance.

Conference Presentation

Citation Download Citation

Andriy Andreyev, Faguo Yang, Matthew Andrew, Lars Omlor, and Herminso Villarraga-Gómez "Spatially variant optimization of an empirical beam-hardening correction algorithm", Proc. SPIE 12242, Developments in X-Ray Tomography XIV, 122421G (10 November 2022); https://doi.org/10.1117/12.2632254

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