X-ray fan beam coded aperture transmission and diffraction imaging for fast material analysis

Abstract

X-ray transmission imaging has been used in a variety of applications for high-resolution measurements based on shape and density. Similarly, X-ray diffraction (XRD) imaging has been used widely for molecular structure-based identification of materials. Combining these X-ray methods has the potential to provide high-resolution material identification, exceeding the capabilities of either modality alone. However, XRD imaging methods have been limited in application by their long measurement times and poor spatial resolution, which has generally precluded combined, rapid measurements of X-ray transmission and diffraction. In this work, we present a novel X-ray fan beam coded aperture transmission and diffraction imaging system, developed using commercially available components, for rapid and accurate non-destructive imaging of industrial and biomedical specimens. The imaging system uses a 160 kV Bremsstrahlung X-ray source while achieving a spatial resolution of ≈ 1 × 1 mm2 and a spectral accuracy of > 95% with only 15 s exposures per 150 mm fan beam slice. Applications of this technology are reported in geological imaging, pharmaceutical inspection, and medical diagnosis. The performance of the imaging system indicates improved material differentiation relative to transmission imaging alone at scan times suitable for a variety of industrial and biomedical applications.

Application of machine learning classifiers to X‐ray diffraction imaging with medically relevant phantoms

“…While each pixel does contain a full XRD spectrum (as an example, XRD spectra from Figure 6h are shown within Figure 6I.i‐iii), colorizing the transmission scan by an intensity‐weighted mean q value from the XRD spectra allows for visual separation of materials that have differences in their mean q values. Here, red pixels contain water and teal pixels contain PLA, with a continuum of colors providing information about the mean q value (i.e., mean atomic spacing of the material present at a given location) 15 …”

“…Here, red pixels contain water and teal pixels contain PLA, with a continuum of colors providing information about the mean q value (i.e., mean atomic spacing of the material present at a given location). 15 It should be emphasized that the ground truth images in Figure 6a-d do not fully identify PLA from water, since for all wells there is a 0.5-mm thickness of PLA below the water for holding the liquid (though this signal relative to 5.5 mm of water is believed to have a negligible impact/be below the sensitivity of the system as observed in the average water spectrum measured in Figure S3). Further, Figure 6c is the most complex phantom given the mixing of water and PLA through the thickness.…”

“…[38][39][40] This X-ray imaging system was used to record the co-registered X-ray transmission and diffraction images used for training and testing in this study. As shown in Figure 1 and discussed in depth in prior work, 15,19 the system consists of a spectrally filtered X-ray tube (XRB Monoblock, Spellman HV, NY) that is spatially collimated to produce a fan beam at the sample plane. The sample is illuminated by the primary fan beam, and both the transmitted and XRD signals are measured at the detector (Xineos-2329, Teledyne Dalsa, CA).…”

“…These phantoms provide well-characterized ground-truths that allow us to investigate the performance of multiple classification algorithms operating on data-rich XRD images. Our previously developed X-ray fan beam-coded aperture imaging system 15,19 is used to obtain XRD images that are fed into the classification algorithms for analysis. For all phantoms, four classification algorithms are compared: cross-correlation (CC), least-squares (LS) unmixing, support vector machines (SVM), and shallow neural networks (SNN).…”

“…XRD imaging approaches have been developed that seek to circumvent sample preparation, which is often destructive to samples and their spatial correlations, while enabling faster measurements with larger fields of view. [11][12][13][14][15] These imaging methods, including energy-dispersive and angular-dispersive raster scanning measurements, coherent scattering computed tomography, and coded aperture systems, seek to measure the XRD signatures of materials throughout a large field of view within reasonable scan times while maintaining adequate spatial resolutions for imaging applications. While simulation studies are often conducted for the design and optimization of these novel imaging architectures, [16][17][18][19] experimental validations on biologically relevant phantoms [20][21][22][23] and real biospecimens 3,6,8,13,15,24 have also occurred.…”