# Enhancing Soft Tissue Differentiation with Different Dual-Energy CT Systems: A Phantom Study
## 4 Discussion
As for the noise, it is observed that it increases as energy decreases. In particular, the Siemens Force was the one with the lowest noise overall. The study also evaluated the RMSE of CT numbers to ensure the robustness of the acquisition technique with respect to the NIST reference standard. An improvement of the RMSE was observed as the VMI energy increased from 40 keV to 140 keV for all DECT systems and tissues. In particular, the RMSE stabilized at energies above 70 keV, showing that high energies can be employed for accurate quantification. An accurate quantification is particularly relevant for clinical applications, such as material characterization, bone mineral density inspection, nondestructive evaluation, and dose calculation, where precision in tissue imaging is essential for the dosimetry. The obtained results show how, when choosing the most appropriate reconstruction energy, it is important to consider various factors depending on whether contrast or accuracy in quantification is preferred, without neglecting the contribution of noise. While low energies may be preferred for better visualization and delineation of some soft tissues, the increase in noise and the reduction in HU accuracy must be carefully considered, particularly in contexts of quantification or other applications requiring high precision. In this case, the use of high energies is recommended. DECT can play a crucial role in optimizing the radiotherapy treatment planning workflow. With its ability to differentiate between different types of tissues and accurately identify tumor boundaries, DECT enables healthcare providers to design targeted treatments that maximize therapeutic efficacy and minimize damage to surrounding healthy tissues. Furthermore, the material decomposition offered by DECT allows for better tissue characterization and a significant reduction in artifacts, ensuring a clearer view of anatomical structures and facilitating treatment planning, especially in cases involving metallic implants or high-density material. In summary, the application of DECT in radiotherapy represents an important technological advancement that has a significant impact on the precision and effectiveness of oncology treatments, enhancing healing prospects and overall patient well-being [26; 39; 47; 48; 49; 50]. Possible applications that emerge from the present study, although they have not yet been clinically tested, may concern breast imaging. This is due to the superior ability of DECT compared to SECT to discriminate glandular and adipose tissues, as well as abdomino-pelvic imaging. Moreover, adding DECT to all those exams that use contrast medium in order to increase vascular enhancement may have a significant aid in diagnostic reporting and radiation dose saving [51; 52]. Research into quantitative imaging biomarkers derived from DECT data, such as iodine concentration, material density, and tissue perfusion parameters, holds promise for applications in disease diagnosis, treatment monitoring, and prognostication. Integration of artificial intelligence (AI) and machine-learning techniques with DECT imaging and derived quantitative imaging biomarkers is expected to play a significant role in future research. AI algorithms can assist in image reconstruction, artifact correction, automated lesion detection and characterization, and personalized treatment planning. Future studies may focus on establishing the clinical significance and predictive value of AI techniques based on DECT imaging in various medical conditions. It is important to acknowledge certain limitations of the study. First, it should be noted that no direct clinical study was conducted to assess the practical applicability of the results obtained. Second, the comparison was limited to a specific group of equipment and DECT modes, involving only two vendors. While different generations of DECT from one vendor (Siemens) were included, the same was not performed for the other vendor (GE).