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The energy-resolved photon counting detector provides the spectral information that can be used to generate images. The novel imaging methods, including the K-edge imaging, projection-based energy weighting imaging and image-based energy weighting imaging, are based on the energy-resolved photon counting detector and can be realized by using various energy windows or energy bins. The location and width of the energy windows or energy bins are important because these techniques generate an image using the spectral information defined by the energy windows or energy bins. In this study, the reconstructed images acquired with K-edge imaging, projection-based energy weighting imaging and image-based energy weighting imaging were simulated using the Monte Carlo simulation. The effect of energy windows or energy bins was investigated with respect to the contrast, coefficient-of-variation (COV) and contrast-to-noise ratio (CNR). The three images were compared with respect to the CNR. We modeled the x-ray computed tomography system based on the CdTe energy-resolved photon counting detector and polymethylmethacrylate phantom, which have iodine, gadolinium and blood. To acquire K-edge images, the lower energy thresholds were fixed at K-edge absorption energy of iodine and gadolinium and the energy window widths were increased from 1 to 25 bins. The energy weighting factors optimized for iodine, gadolinium and blood were calculated from 5, 10, 15, 19 and 33 energy bins. We assigned the calculated energy weighting factors to the images acquired at each energy bin. In K-edge images, the contrast and COV decreased, when the energy window width was increased. The CNR increased as a function of the energy window width and decreased above the specific energy window width. When the number of energy bins was increased from 5 to 15, the contrast increased in the projection-based energy weighting images. There is a little difference in the contrast, when the number of energy bin is increased from 15 to 33. The COV of the background in the projection-based energy weighting images is only slightly changed as a function of the number of energy bins. In the image-based energy weighting images, when the number of energy bins were increased, the contrast and COV increased and decreased, respectively. The CNR increased as a function of the number of energy bins. It was concluded that the image quality is dependent on the energy window, and an appropriate choice of the energy window is important to improve the image quality.

In this study the correlation between bainitic microstructure and the low-temperature toughness of high-strength API pipeline steels was discussed in terms of crack initiation and propagation in the microstructure. Three types of API pipeline steels with different bainitic microstructures were fabricated using varying alloying elements and thermo-mechanical processing conditions, and then their microstructure was characterized by optical and scanning electron microscopy, and electron backscatter diffraction (EBSD). In particular, the effective grain size and microstructure fraction of the steels were quantitatively measured by EBSD analysis. Although all the steels were composed of polygonal ferrite (PF), and complex bainitic microstructures such as acicular ferrite (AF), granular bainite (GB), and bainitic ferrite (BF), they had different effective grain sizes and microstructure fraction, depending on the alloying elements and thermomechanical processing conditions. Charpy impact test results showed that when the martensite-austenite constituent fraction was lowest, it resulted in higher upper-shelf energy, and absorbed energy at room temperature due to the decrease in crack initiation. In contrast, excellent low-temperature toughness, such as lower ductile-brittle transition temperature and higher absorbed energy at low temperatures, could be achieved with a bainitic microstructure with fine effective grain size and high fraction of high-angle grain boundaries, which act as obstacles to prevent cleavage crack propagation.

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