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Abstract
Phase contrast X-ray tomography (PCT) enables the study of systems consisting of elements with similar atomic numbers. Processing datasets acquired using PCT is nontrivial because of the low-pass characteristics of the commonly used single-image phase retrieval algorithm. In this study, we introduce an image processing methodology that simultaneously utilizes both phase and attenuation components of an image obtained at a single detector distance. This novel method, combined with regularized Perona-Malik filter and bias-corrected fuzzy C-means algorithm, allows for automated segmentation of data acquired through four-dimensional PCT. Using this integrated approach, the three-dimensional coarsening morphology of an Aluminum-29.9 wt% Silicon alloy can be analyzed.
Highlights
Phase contrast x-ray tomography (PCT) enables the study of weakly absorbing samples, as well as systems consisting of elements with similar atomic numbers
This is because the real part of the refractive index δ dominates over the imaginary part β in PCT experiments [1, 2]
In propagation-based PCT, a phase map is commonly obtained by applying phase-retrieval algorithms to the projections collected by a similar setup to Fig. 1 [3, 4]
Summary
Phase contrast x-ray tomography (PCT) enables the study of weakly absorbing samples, as well as systems consisting of elements with similar atomic numbers. This is because the real part of the refractive index δ dominates over the imaginary part β in PCT experiments [1, 2]. Filtered back projection [5] is applied to these phase maps in order to recover the refractive index decrement during reconstruction This two-step approach [6] of phase-retrieval followed by backprojection will hereafter be referred to as PAG [3]. Robust segmentation of the PCT reconstructions is crucial for quantitative analysis of 3D structures, e.g., surface area of interfaces and interfacial curvature measurements [7]
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