Implementation and assessment of the black body bias correction in quantitative neutron imaging

Chiara Carminati; Pierre Boillat; Florian Schmid; Peter Vontobel; Jan Hovind; Manuel Morgano; Marc Raventos; Muriel Siegwart; David Mannes; Christian Gruenzweig; Pavel Trtik; Eberhard Lehmann; Markus Strobl; Anders Kaestner

doi:10.1371/journal.pone.0210300

Implementation and assessment of the black body bias correction in quantitative neutron imaging

PLoS One. 2019 Jan 4;14(1):e0210300. doi: 10.1371/journal.pone.0210300. eCollection 2019.

Authors

Affiliations

¹ Laboratory for Neutron Scattering and Imaging, Paul Scherrer Institut, Villigen, Switzerland.
² Electrochemistry Laboratory, Paul Scherrer Institut, Villigen, Switzerland.

Abstract

We describe in this paper the experimental procedure, the data treatment and the quantification of the black body correction: an experimental approach to compensate for scattering and systematic biases in quantitative neutron imaging based on experimental data. The correction algorithm is based on two steps; estimation of the scattering component and correction using an enhanced normalization formula. The method incorporates correction terms into the image normalization procedure, which usually only includes open beam and dark current images (open beam correction). Our aim is to show its efficiency and reproducibility: we detail the data treatment procedures and quantitatively investigate the effect of the correction. Its implementation is included within the open source CT reconstruction software MuhRec. The performance of the proposed algorithm is demonstrated using simulated and experimental CT datasets acquired at the ICON and NEUTRA beamlines at the Paul Scherrer Institut.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Bias
Computer Simulation
Copper / chemistry
Databases, Factual
Image Processing, Computer-Assisted / methods*
Image Processing, Computer-Assisted / statistics & numerical data
Lead / chemistry
Neutron Diffraction / methods*
Neutron Diffraction / statistics & numerical data
Neutrons*
Phantoms, Imaging
Software
Tomography, X-Ray Computed / statistics & numerical data
Water / chemistry

Substances

Water
Lead
Copper

Grants and funding

This project receives funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654000, recipient Dr. C.Carminati. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.