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Image reconstruction from Compton scattering measurements results in a nonlinear inverse problem that can produce multiple solutions, particularly in deep/dense objects. In this work, the nonlinearity of the problem is mitigated by a transformation which minimizes the difference of the ratios of measurements with overlapping fields of view. Overlapping eliminates one of the competing factors in the nonlinear problem, and allows solving the inverse problem of image reconstruction beyond the one-mean-free path equivalent depth previously needed to reach a solution in scatter-only imaging. The method also enables the use of wide X-ray fan beam sources, and is numerically applied to exploit the ability of scatter imaging to acquire single-side two-dimensional (2-D) and three-dimensional (3-D) images of relatively dense objects. Examples are presented to demonstrate the method's ability to deal with noisy measurements, at various degrees of overdetermination. [ABSTRACT FROM AUTHOR]

A 3-D X-ray imaging system that eliminates the rotation process associated with tomographic systems is introduced. The system relies on measuring the intensity of Compton scattered radiation in two directions mutually perpendicular to an incident beam that rectilinearly scans the object. These measurements, along with transmission measurements obtained from one-side exposure of the object are utilized to reconstruct 3-D images of three physical parameters: two attenuation coefficients corresponding to the incident and scattered energies, and the electron-density in each voxel. This part of the paper addresses the theoretical and physical aspects associated with the image reconstruction process, and presents examples of images reconstructed from experimental results. [ABSTRACT FROM AUTHOR]

The design aspects of a 3-D X-ray imaging system that eliminates the rotation process associated with tomographic systems are discussed. The processes of selecting the proper X-ray voltage and the beam configuration are addressed. A rectilinear scanning mechanism is introduced. The choice and design of detector assemblies are described. The paper presents the results of experiments devised to progressively test the performance and response of the system. [ABSTRACT FROM AUTHOR]

The quality of computed-tomography (CT) images deteriorates when images are reconstructed from incomplete data. This work makes use of the a priori knowledge inherent in the membership functions and the logical rules of a fuzzy inference system (FIS) to compensate for the missing data. It is shown that a fuzzy inference system can be used to improve the quality of reconstructed CT images, particularly when the images are reconstructed from incomplete data. It is proposed to reconstruct a coarser image for which the data is over-complete, and use the histograms of this image and that of the original finer image to generate the membership functions required in FIS. The two images are then fused, with the aid of logical rules based on the knowledge that the two images posses the same distinct attributes (pixel values). In order to avoid the difference in spatial resolution between the original fine image and the reconstructed coarse image, a modified FIS method is introduced to refine the fine image. Results are presented, showing visually and quantitatively that this FIS refinement process improves the quality of the original fine image., Competing Interests: The authors declare no conflict of interest., (© 2021 The Authors.)

Considerable progress has been recently made on using naturally occurring radiation in the form of muons and neutrinos in a wide range of applications. However, since the discovery of radioactivity, natural radiation has found a number of imaging applications in a variety of fields. The purpose of this paper is to provide an overview of the many applications in which radiation emitted from naturally-occurring radioactive materials is used to image materials and structures, as well as to review the recent progress in cosmic-radiation imaging. It is hoped that this review will alert practitioners to the valuable information that can be readily obtained without the use of artificial sources, and to motivate researcher to develop and explore further applications., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

A method is presented for interpreting the values of x-ray attenuation coefficients reconstructed in computed tomography of porous media, while overcoming the ambiguity caused by the multichromatic nature of x-rays, dilution by void, and material heterogeneity. The method enables determination of porosity without relying on calibration or image segmentation or thresholding to discriminate pores from solid material. It distinguishes between solution-accessible and inaccessible pores, and provides the spatial and frequency distributions of solid-matrix material in a heterogeneous medium. This is accomplished by matching an image of a sample saturated with a contrast solution with that saturated with a transparent solution. Voxels occupied with solid-material and inaccessible pores are identified by the fact that they maintain the same location and image attributes in both images, with voxels containing inaccessible pores appearing empty in both images. Fully porous and accessible voxels exhibit the maximum contrast, while the rest are porous voxels containing mixtures of pore solutions and solid. This matching process is performed with an image registration computer code, and image processing software that requires only simple subtraction and multiplication (scaling) processes. The process is demonstrated in dolomite (non-uniform void distribution, homogeneous solid matrix) and sandstone (nearly uniform void distribution, heterogeneous solid matrix) samples, and its overall performance is shown to compare favorably with a method based on calibration and thresholding., (Copyright © 2014 Elsevier Ltd. All rights reserved.)