Location

La Jolla ,CA

Start Date

1-1-1989 12:00 AM

Description

In earlier works [1,2] it was concluded that image reconstruction from incomplete data can be achieved through an iterative transform algorithm which utilizes the a priori information on the object to compensate for the missing data. The iterative transform algorithm is schematically illustrated in Fig. 1. The image is transformed back and forth between the object space and the projection space, being corrected by the a priori information on the object in the object space, and by the known projections in the projection space. The a priori information in the object space includes a boundary enclosing the object, and an upper bound and a lower bound of the object density. Among these three pieces of a priori information, the object-enclosing boundary is the most important one; experience indicated that the upper and lower bound constraints usually make 1–2% difference in the image quality. The closer the enclosing boundary matches the actual shape of the object, the better will be the quality of the reconstructed image.

Book Title

Review of Progress in Quantitative Nondestructive Evaluation

Volume

8A

Chapter

Chapter 2: Advanced Techniques

Section

X-Ray Computed Tomography

Pages

407-414

DOI

10.1007/978-1-4613-0817-1_52

Language

en

File Format

application/pdf

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Jan 1st, 12:00 AM

Incomplete-Data Image Reconstructions in Industrial X-ray Computerized Tomography

La Jolla ,CA

In earlier works [1,2] it was concluded that image reconstruction from incomplete data can be achieved through an iterative transform algorithm which utilizes the a priori information on the object to compensate for the missing data. The iterative transform algorithm is schematically illustrated in Fig. 1. The image is transformed back and forth between the object space and the projection space, being corrected by the a priori information on the object in the object space, and by the known projections in the projection space. The a priori information in the object space includes a boundary enclosing the object, and an upper bound and a lower bound of the object density. Among these three pieces of a priori information, the object-enclosing boundary is the most important one; experience indicated that the upper and lower bound constraints usually make 1–2% difference in the image quality. The closer the enclosing boundary matches the actual shape of the object, the better will be the quality of the reconstructed image.