Object Specific Trajectory Optimization for Industrial X-ray Computed Tomography.

In industrial settings, X-ray computed tomography scans are a common tool for inspection of objects. Often the object can not be imaged using standard circular or helical trajectories because of constraints in space or time. Compared to medical applications the variance in size and materials is much larger. Adapting the acquisition trajectory to the object is beneficial and sometimes inevitable. There are currently no sophisticated methods for this adoption. Typically the operator places the object according to his best knowledge. We propose a detectability index based optimization algorithm which determines the scan trajectory on the basis of a CAD-model of the object. The detectability index is computed solely from simulated projections for multiple user defined features. By adapting the features the algorithm is adapted to different imaging tasks. Performance of simulated and measured data was qualitatively and quantitatively assessed.The results illustrate that our algorithm not only allows more accurate detection of features, but also delivers images with high overall quality in comparison to standard trajectory reconstructions. This work enables to reduce the number of projections and in consequence scan time by introducing an optimization algorithm to compose an object specific trajectory.

Data fusion in X-ray computed tomography using a superiorization approach.

X-ray computed tomography (CT) is an important and widespread inspection technique in industrial non-destructive testing. However, large-sized and heavily absorbing objects cause artifacts due to either the lack of penetration of the specimen in specific directions or by having data from only a limited angular range of views. In such cases, valuable information about the specimen is not revealed by the CT measurements alone. Further imaging modalities, such as optical scanning and ultrasonic testing, are able to provide data (such as an edge map) that are complementary to the CT acquisition. In this paper, a superiorization approach (a newly developed method for constrained optimization) is used to incorporate the complementary data into the CT reconstruction; this allows precise localization of edges that are not resolvable from the CT data by itself. Superiorization, as presented in this paper, exploits the fact that the simultaneous algebraic reconstruction technique (SART), often used for CT reconstruction, is resilient to perturbations; i.e., it can be modified to produce an output that is as consistent with the CT measurements as the output of unmodified SART, but is more consistent with the complementary data. The application of this superiorized SART method to measured data of a turbine blade demonstrates a clear improvement in the quality of the reconstructed image.

Ultrasonic imaging of complex specimens by processing multiple incident angles in full-angle synthetic aperture focusing technique.

In the evaluation of large-scale metallic specimens, X-ray CT suffers from limited penetration, which results in artifacts in the reconstructed image. Data fusion of information obtained by different modalities allows correction of those artifacts. In this contribution, an approach is presented to provide complementary data of the inner pattern of the specimen by ultrasonic testing in immersion mode. To process an ultrasonic imaging full-angle synthetic aperture focusing technique, data are acquired along the a priori known contour of the specimen. Substantial discrepancies in speed of sound between the couplant and the material of the specimen lead to refraction effects which are corrected by a virtual source element method. Furthermore, several incident angles at each virtual source are utilized to achieve an enhanced detectability of inner structural edges. However, arising reverberations limit image quality and must be suppressed by predictive deconvolution. Additionally, a subspace analysis and projection method is utilized to remove echoes of the a priori known surface in the reconstructed image which potentially mask information of near-surface structures. In comparison with exclusively perpendicular insonification, resulting images show a significant enhanced possibility of detection for inner structural edges even in adverse orientations for ultrasonic imaging. Furthermore, surface echoes and reverberations are suppressed by the proposed filter methods in a reliable way.