# Dual-Energy CT Applications in Urological Diseases
## 2 DECT Principles  
In CT scanners, X-rays are generated in the X-ray tube by focusing an electron stream emitted from the cathode into a narrow beam that bombards a small focal spot on the tungsten target anode. These beams are made up of photons with a wide continuum of energies, referred to as \"polychromatic X-rays\", which create the X-ray spectrum. The maximum photon energy in the X-ray spectrum matches the X-ray tube kilovoltage, meaning that the maximum energy of a 120 kV tube voltage is 120 keV. The X-ray spectrum varies depending on the tube voltage, and the effective X-ray energy is often used as a representative value of a polychromatic X-ray photon spectrum [5]. The effective energy is the energy of a polychromatic X-ray expressed as the energy of a monochromatic X-ray with equivalent interactions, which can be measured using an absorber composed of aluminum or copper [6]. In DECT, virtual monochromatic images (VMIs) obtained at 65-70 keV have the same CT attenuation number (HU) as single-energy CT images obtained at 120 kVp [7]. Therefore, VMIs acquired at 65-70 keV can provide almost equivalent information to single-energy CT images acquired at 120 kVp. The fundamental principle of DECT is to achieve a good contrast resolution between different materials by exploiting their different spectral properties, that is, differences in the attenuation of X-rays at different photon energies. At different energy levels, a material has a varying CT number (in Hounsfield units, HU), and the degree of this difference relies on the elemental composition. The CT number of a material is related to its linear attenuation coefficient and is not unique for each material, as similar CT numbers can characterize even materials with different compositions. For this reason, due to their substantial overlap in CT density values, it might be challenging to discern between different materials on single-energy CT scans which therefore provide limited information on tissue composition [8]. DECT images can allow the differentiation and quantification of materials with different elemental compositions by comparing their CT numbers at two different energy levels, and this capability makes DECT a useful tool in the characterization of various materials in a tissue sample. Different dual-energy technologies are available. Dual-energy CT scanning typically involves two different X-ray energies, usually in the range of 70-100 kVp and 135-150 kVp. Some scanners use two independent X-rays through the fast tube-voltage switching, sequential scan, and dual-source CT systems [9]. Other vendors, however, use only one X-ray source and separate the beam into low- and high-energy spectra either at the detector level (dual-layer system) [10] or at the tube output (split filter system). Dual-energy analysis methods can be categorized into image data-based analysis and raw data-based analysis. The image-based analysis involves post-processing of the dual-energy scans either before or after the reconstruction of high- and low-energy images to create various dual-energy CT applications [11]. The spatial matching of the reconstructed images is more important than perfectly matching the X-ray paths for high- and low-tube voltages in image-based analysis. Dual-energy data are processed after image reconstruction to create blended images, which are weighted average images at various tube voltages. Other applications include creating iodine map images through material decomposition and virtual non-contrast images by subtracting iodine map images from weighted average images. The raw data-based analysis involves matching the X-ray paths for high- and low-tube voltages exactly. Material raw data, such as iodine and water, or bone and water, are processed directly through material decomposition, and image reconstruction is then performed. This approach assumes that the human body contains a mixture of two different materials, typically iodine and water, and calculates the content of each material from the original raw dataset.