# Applications of Medical Physics
## 2 Contributions
The analysis suggests that low-dose EOS digital radiographic evaluation has the same capability as conventional radiography to detect and measure echondroma-like inserts on a phantom. Thanks to its lower radiation dose, this imaging method could be particularly useful in the follow-up of patients affected by multiple echondromas. The study of [PERSON] et al. [PERSON] et al. (2018) focuses on the possibility of reducing organ doses in pediatric CT chest-abdominal pelvis examination by optimizing the CT protocols. The effect of scan parameters such as tube voltage, tube current, slice collimation, pitch, and tube current modulation on image quality indices and organ doses are accordingly investigated. The results suggest that a significant potential reduction in the dose for such type of diagnostic examinations is achievable. Dose optimization in CT scans is also considered in the manuscript by [PERSON] et al. [PERSON] et al. (2018). The authors highlight the need for an exhaustive characterization of noise properties in CT imaging with iterative reconstruction (IR) algorithms for assessing the actual dose-reduction potential of this technique, as well as its possible limitations for specific clinical applications. In their in-phantom study, the authors carry out a voxel-based assessment of noise properties in CT imaging with adaptive statistical iterative reconstruction (ASiR) and adaptive statistical iterative reconstruction-V (ASiR-V) for different blending levels of reconstruction and contrast objects. Their results confirm the potential of ASiR-V and ASiR in reducing noise as compared with conventional filtered back projection approaches, suggesting, however, that the use of pure ASiR-V or ASiR might be suboptimal for specific clinical applications. In conclusion, authors recommend a voxel-based characterization of noise properties when dealing with a novel IR algorithm or when comparing the performances of different IR algorithms. Quantitative imaging is another recurrent topic in various contributions of this Special Issue. Actually, the quantitative analysis of medical images proved to be a powerful approach for improving diagnostic and treatment strategies, as well as for developing predictive models. Texture analysis is performed by [PERSON] et al. [9] to characterize the impact of the acquisition angle on Digital Breast Tomosynthesis (DBT) images. The authors conclude that the DBT acquisition angle affects the textures extracted from DBT images, and this dependence should be considered when establishing baselines for classifiers of malignant tissue. Therefore, texture analysis in DBT has the potential to be an alternative to other quantitative techniques such as model observer methods for scoring and comparing DBT images. Quantitative analyses are also increasingly used in nuclear medicine, both for diagnostic and therapeutic purposes. [PERSON] et al. [10] highlight how the Partial Volume Effect (PVE) is the most important factor of loss of quantification in Nuclear Medicine. Therefore, in order to achieve an accurate quantification of the radioactivity concentration, PVE has to be considered and compensated, especially in small structures. In this context, the authors present a new approach for the correction of PVE, using a post-reconstruction process starting from a mathematical expression, which only requires the knowledge of the Full Width at Half Maximum of the Point Spread Function of the imaging system. In the domain of low-field MR-guided radiotherapy for cervical cancer, [PERSON] et al. [11] evaluate the performance of the radiobiological parameter Early Regression Index \(\text{ERI}_{\text{TCP}}\) in predicting pathological complete response. The results suggest that \(\text{ERI}_{\text{TCP}}\), calculated by combining the tumor volume measured on the MR images acquired at simulation and during therapy, is a promising response biomarker for cervical cancer.