# New-Generation ASiR-V for Dose Reduction While Maintaining Image Quality in CT: A Phantom Study
## 4 Discussion
The difference between their findings and ours may be partly related to the lower noise index they selected for ASiR-V acquisitions (19 in that study vs. 22, 24, and 30 in our study). Our results showed that the use of ASiR-V does not enable the recovery of the same image quality obtained using ASiR or FBP algorithms when dealing with CT images with NI30. This is likely due to the fact that an NI of 30 was too high such that the corresponding dose reduction severely affected the image quality of the reconstructed data, as reported in a previous study [31]. Indeed, ASiR-V NI30 images were graded by the radiologists as too noisy to provide diagnostic information, unless for specific clinical indications concerning high-contrast lesions, such as renal stones. The ASiR-V NI30 series was also often described as having an overly smoothed appearance, in line with previous findings [39]. Radiologists use visual detection, pattern recognition, memory, and cognitive reasoning to synthesize final interpretations of radiologic studies. Against this backdrop, one might speculate that the radiologists' perception of the poor image quality of ASiR-V NI30 images could be partially ascribed to the fact that for years, FBP images have been the basis for image reconstruction, and the subjective assessment of image quality may be somehow biased. As a matter of fact, the IR algorithm, new-generation scanners, and the related obtained images are relatively new and not yet available everywhere (this is especially true for small diagnostic centers). The main finding of this study is that obtaining images of appropriate/sufficient quality while highly reducing the delivered ionizing dose is possible, and the noise and/or artifacts eventually introduced in the images do not affect the clinical reading. This could be particularly useful, for example, in repeated follow-up exams, in order to preserve patients from receiving unnecessary radiation exposures. A possible limitation of the present study is that it is completely based on phantom acquisitions, which only include regularly shaped inserts. In future work, investigating the potential effect of irregularly shaped objects or more realistic anatomical details might prove important. Another limitation is that the evaluation of new ASiR-V performance in a phantom is not sufficient for the complete evaluation and optimization of clinical images. Further studies using an anthropomorphic body phantom with embedded low-contrast lesions would be required for a robust evaluation of ASiR-V in clinical routine. Additionally, some form of texture analysis could be employed in the future to improve the quantitative analysis of image quality. For example, filtration-histogram-based texture analysis can be used, which is extensively described in the literature for human studies in cancer diagnosis, prognosis, and treatment response on CT [40]. Patient studies have demonstrated that the use of spatial scale filters (e.g., SSF = 2-6 mm, which is greater than twice the resolution of a CT image, e.g., submillimeter voxel) facilitates the extraction of features related to tissue/tumor heterogeneity, such as object size, density, and concentration [39]. However, if the SSF is tuned to the exact resolution of the CT images (e.g., SSF = 1 mm), resulting in the quantification of the filtered images using statistical and histogram metrics, it could potentially pick up/reflect features (texture) related to image noise. This provides a good starting point for discussion and further research. In terms of the future outlook, further studies on dose reduction and the optimization of image quality could prove quite beneficial to the literature.