Tube-based-polarized energy-dispersive X-ray fluorescence (EDXRF) is a powerful adaptation on traditional EDXRF, requiring very specific geometry and a scattering target to generate polarized X-rays. This secondary target is typically chosen to be a metallic foil, allowing for the polarization of the incident X-ray beam, and the addition of the secondary target's fluorescence response to the initial beam. A simulation, using GEANT4 Monte Carlo code, and an experimental confirmation were used to determine the optimal thickness of a metallic secondary target for use in tube-based-polarized EDXRF. The optimal thickness was determined by looking at the signal-to-noise ratio (SNR). Using the results, the optimal thickness and tube potential were calculated for the common secondary target materials Cu, Mo, and Sn, when looking at an Fe sample. The optimal thickness results were compared with the results when using an 'infinitely thick' target. The results show improvements in SNRs of 6 17%, illustrating the potential benefits of such calculations. Additionally, the optimal collimation of a polarized EDXRF system was examined, and it was found that increasing the total count rate should be the primary goal of geometrical optimization. If the count rate of the experimental setup is limited by tube output, then having the largest possible collimators yielded the maximum SNR. In contrast, if the count rate is limited by detector dead time, then decreasing the collimator size between secondary target and sample provided the maximal SNR.
Previous studies have shown that the combination of X‐ray fluorescence and X‐ray diffraction data can be used as a histopathological characterization tool for breast tissue. Recent advances in energy‐dispersive X‐ray fluorescence techniques have allowed for benchtop systems to produce useful results in a reasonable time frame, allowing for clinical implementation to be realized. Using a polarized energy‐dispersive X‐ray fluorescence and energy‐dispersive X‐ray diffraction system optimized for measuring soft tissues, 38 breast tissue samples (19 normal and 19 diseased) were interrogated. The measured elemental concentrations and adipose and fibrous tissue contents were used in a principal component analysis study to determine the variables that produced the most differentiation between the normal and diseased tissues. For each sample, a soft independent modeling of class analogy technique was utilized to create classification models using the K, Fe, and Zn concentration and adipose and fibrous tissue content of all other breast samples. The class model produced from both X‐ray fluorescence and X‐ray diffraction data correctly classified 31 of 38 samples with no false positives or false negatives, showing improvement from solely X‐ray fluorescence models or X‐ray diffraction models alone, and demonstrates the usefulness of such a technique.
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