Correction of distortion from image intensifier X-ray detector for CT (computed tomography) reconstruction

• Main Author: Karuppiah Vellarasan Aaron.
• Other Authors: Wong, Brian Stephen
• Format: Final Year Project
• Language: English
• Published: 2009
• Subjects: DRNTU::Engineering::Mechanical engineering
• Online Access: http://hdl.handle.net/10356/16138
• Institution: Nanyang Technological University

X-ray is an important tool used in Non-destructive testing in many various industries ranging from aerospace to electronics. Conventional X-ray techniques were limited to 2 dimensional (2D) analyses and thus limited the information about defects that could be obtained. The improvement of digital X-ray technologies led to the creation of Computed Tomography (CT) a very powerful X-ray technique where the sample could be X-rayed and analysed in all 3 dimensions (3D). The vast majority of NDT X-ray systems in operation today are Image Intensifier (II) systems. II systems are suitable for 2D analyses but not CT, due to the inherent distortion in the image geometries. The value of CT in the NDT field is widely recognised and attempts to upgrade the 2D II systems to 3D CT compatible systems have been made. The CT images from these systems are inadequate as the 3D images are blurred and indistinct. Therefore the distortion issues of the II systems must first be addressed before an acceptable CT image from these modified systems can be attained. This report covers the experimental methodology to map out the distortions in an image taken by a NDT II system and introduces an algorithm to correct the distortions by digital imaging processing. The distortions were mapped out and studied from an image of a precision fabricated aluminium grid of dimension 39.9 by 39.9 mm for the outer square and 0.7 by 0.7 mm for each inner square. The algorithm firstly identifies the image centre and calculates the correlation between the original distorted image matrix and the ideal image matrix. Upon computing the correlation the ideal image matrix is mapped onto the original image matrix and takes on the corresponding greylevel values to produce the corrected image. The corrected images are then used to reconstruct a sample in CT mode to test the effectiveness of the correction algorithm.