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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