3D Reconstruction from Sparse Radiographic Data
Nondestructive evaluation of materials through X-ray and -y-ray radiography has long been achieved by inferring three-dimensional structure from exposed films. Multiple views with varying positions of radioactive sources and the film have the potential for direct three-dimensional tomographic reconstruction for more detailed diagnosis of material flaws. The data are sufficiently sparse, however, to leave the reconstruction badly under-specified, requiring regularization and/ or constraints to achieve meaningful results. In this chapter we discuss and illustrate the application of Bayesian binary 3D tomographic reconstruction to radiographs, including the several non-idealities frequently encountered in the field.
KeywordsAttenuation Radioactive Isotope Expense Convolution Radon
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