X-ray and gamma-ray computed tomography for industrial nondestructive testing and evaluation

Ian Costello; Peter Wells; John R. Davis; Nino Benci; David Skerrett; D. Rhys Davies

doi:10.1117/12.171265

28 March 1994 X-ray and gamma-ray computed tomography for industrial nondestructive testing and evaluation

Ian Costello, Peter Wells, John R. Davis, Nino Benci, David Skerrett, D. Rhys Davies

Proceedings Volume 2092, Substance Detection Systems; (1994) https://doi.org/10.1117/12.171265
Event: Substance Identification Technologies, 1993, Innsbruck, Austria

Abstract

This paper presents an overview of two recently constructed computed tomography (CT) scanners that have been designed to provide structural information for industrially relevant materials and components. CT enables cross-sectional slices of an object to be nondestructively imaged and represented as a map of linear attenuation coefficient. As linear attenuation is the product of mass attenuation and density, this usually enables a straightforward interpretation of the image in terms of density. The two instruments are a transportable scanner using a 160 kV(peak) powered x-ray tube for the inspection of wooden power poles up to 450 mm in diameter, and an industrial scanning system designed around an Ir-192 gamma-ray source for materials characterization and the testing and evaluation of castings, ceramics, and composites. The images presented in this paper have generally been reconstructed using the summation convolution back-projection (SCBP) method, and this technique is outlined. Direct Fourier reconstruction is also used and compared with the SCBP method. A brief discussion is offered on incorporating edge detection methods into the image reconstruction process for the improved identification of defects such as cracks and voids.

Citation Download Citation

Ian Costello, Peter Wells, John R. Davis, Nino Benci, David Skerrett, and D. Rhys Davies "X-ray and gamma-ray computed tomography for industrial nondestructive testing and evaluation", Proc. SPIE 2092, Substance Detection Systems, (28 March 1994); https://doi.org/10.1117/12.171265

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