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Mitigating artifacts by data driven identification & correction of rotational misalignment in gamma ray computed tomography.

Authors :
Acharya R
Kumar U
Patankar VH
Kar S
Dash A
Source :
Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine [Appl Radiat Isot] 2020 Dec; Vol. 166, pp. 109408. Date of Electronic Publication: 2020 Sep 08.
Publication Year :
2020

Abstract

Industrial Computed Tomography (ICT) is a radiation based cross sectional imaging technique that requires a sample to be manipulated precisely in a specific geometry to acquire analytically useful data. Unlike medical CT, industrial CT may require use of gamma radiation from radio-isotopes like Co <superscript>60</superscript> , Cs <superscript>137</superscript> etc. having higher energy radiations for penetrating through higher density and thickness of material under inspection. Data acquisition in ICT involves use of a mechanical manipulator to rotate either the specimen or the source and detectors assembly in circular and linear geometry. Misalignment in mechanical set-up leads to significant artifacts in CT image. The effects may be even more pronounced in data acquired with discrete detector as against Linear Detector Array (LDA) because of certain built-in mechanical integrity associated with LDA. This paper discusses cross correlation based software correction method for combination of gamma ray source and NaI (Tl) scintillation detector based transmission ICT system in parallel beam CT geometry. The proposed correction does not require calibration of the set-up and any prior knowledge of the sample geometry or composition. This data driven correction yields improved CT reconstruction with limited data. The method is demonstrated with a mathematical simulation and applied to experimental data for validation.<br /> (Copyright © 2020. Published by Elsevier Ltd.)

Details

Language :
English
ISSN :
1872-9800
Volume :
166
Database :
MEDLINE
Journal :
Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
Publication Type :
Academic Journal
Accession number :
32971424
Full Text :
https://doi.org/10.1016/j.apradiso.2020.109408