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The last 25 years have seen an explosion in the number of new x-ray based imaging methods and applications. In reviewing these new capabilities we see that the ability to generate improved data quality underlies a number of the major advances. Improvement in the quality of x-ray sources, in detector resolution and dynamic range, in data analysis methods both in image processing and in reconstruction techniques have yielded significant advances in image quality. In addition to the improved imaging hardware, the use of computers play a key role in extending capabilities in high speed computed tomography, in real time image enhancement, in automated defect recognition and in the development of accurate x-ray inspection simulations. The ability to generate information and to analyze the complexities introduced by typical inspection demands can quickly outstrip even today's computational resources. Developments in parallel computing and computer memory are beginning to managing the huge data volumes now routinely produced and open up a number of new applications. Some examples include high-resolution 3D-image generation with the capability to probe micron length scales, dual energy computed tomography providing improved airport security, materials characterization techniques providing new tool for process development, and x-ray image formation modeling. The ability to model the details of the generation of bremsstrahlung radiation and its interaction with the complex geometry of the object under consideration provide for the first time a means to determine and quantify the optimal parameters for an inspection. These advances over the last 25 years represent exciting time in x-ray imaging, the impact of which will be played out in the next decade. ©2001 American Institute of Physics.
Standard radiography simulators are based on the attenuation law complemented by built-up-factors (BUF) to describe the interaction of radiation with material. The assumption of BUF implies that scattered radiation reduces only the contrast in radiographic images but does not image object structures itself. This simplification holds for a wide range of applications like weld inspection as known from practical experience. But only a detailed description of the different underlying interaction mechanisms is capable to explain effects like mottling or others that every radiographer has experienced in practice. The application of the N-Particle Monte Carlo code MCNP is capable to handle primary and secondary interaction mechanisms contributing to the image formation process like photon interactions (absorption, incoherent and coherent scattering including electron-binding effects, pair production) and electron interactions (electron tracing including X-Ray fluorescence and Bremsstrahlung production). Additionally it opens up possibilities like the separation of influencing factors and the understanding of the functioning of intensifying screen used in film radiography. The paper intends to discuss the opportunities in applying the Monte Carlo method to investigate special features in radiography in terms of selected examples. It is important to note that the use of Monte Carlo methods is a laboratory type of technique for basic investigations because of the enormous computing power that is needed. For in-field applications such as for inspection planing simplified models are of much greater importance and increasingly in use. ©2003 American Institute of Physics
The paper presents a special reconstruction algorithm that is capable to monitor density differences in multi-phase flows. The flow cross section is represented as discrete dynamic random field. A fixed gray value is assigned to each flow phase characterizing the material property of the phase. The image model is given by a set of non-linear stochastic difference equations. The corresponding inversion task is not accessible by common tomographic techniques applying reconstruction algorithms like filtered backprojection or algebraic reconstruction technique (ART). The developed algorithm is based on the Kalman filter technique adapted to non-linear phenomena. The average velocity distribution together with the corresponding covariance matrix of the liquid flow through a pipe serves as prior information in statistical sense. To overcome the non-linearity in the process model as well as in the measurement model the statistical linearization technique is applied. Moreover the Riccati equation, giving the error covariance matrix, and the equation for the optimal gain coefficients can be solved in advance and later used in the filter equation. It turns out that the resulting reconstruction or filter algorithm is recursive, i.e. yielding the quasi-optimal solution to the formulated inverse problem at every reconstruction step by successively counting for the new information collected in the projections. The applicability of the developed algorithm is discussed in terms of characterizing or monitoring a multi-phase flow in a pipe. ©2003 American Institute of Physics