Abstract: High-energy X-ray computed tomography (CT) systems have been
recently used to produce high-resolution images in various nondestructive
testing and evaluation (NDT/NDE) applications. The accuracy of the dimensional
information extracted from CT images is rapidly approaching the accuracy
achieved with a coordinate measuring machine (CMM), the conventional approach
to acquire the metrology information directly. On the other hand, CT systems
generate the sinogram which is transformed mathematically to the pixel-based
images. The dimensional information of the scanned object is extracted later by
performing edge detection on reconstructed CT images. The dimensional accuracy
of this approach is limited by the grid size of the pixel-based representation
of CT images since the edge detection is performed on the pixel grid. Moreover,
reconstructed CT images usually display various artifacts due to the underlying
physical process and resulting object boundaries from the edge detection fail
to represent the true boundaries of the scanned object. In this paper, a novel algorithm to reconstruct the boundaries of
an object with uniform material composition and uniform density is presented.
There are three major benefits in the proposed approach. First, since the
boundary parameters are reconstructed instead of image pixels, the complexity
of the reconstruction algorithm is significantly reduced. The iterative
approach, which can be computationally intensive, will be practical with the
parametric boundary reconstruction. Second, the object of interest in metrology
can be represented more directly and accurately by the boundary parameters
instead of the image pixels. By eliminating the extra edge detection step, the
overall dimensional accuracy and process time can be improved. Third, since the
parametric reconstruction approach shares the boundary representation with
other conventional metrology modalities such as CMM, boundary information from
other modalities can be directly incorporated as prior knowledge to improve the
convergence of an iterative approach. In this paper, the feasibility of
parametric boundary reconstruction algorithm is demonstrated with both simple
and complex simulated objects. Finally, the proposed algorithm is applied to
the experimental industrial CT system data.
