###### Abstract
The ASiR algorithm works on sinogram data by modeling the photon statistics and some noise-related properties of the scanned object (e.g., scatter), and can be employed with different FBP-ASiR blending levels [36]. Although ASiR is widely adopted due to its fast reconstruction time, some image quality properties, such as image texture and spatial resolution, depend on the blending level of reconstruction and the dose level, yielding some artificial image texture and degradation of spatial resolution at a higher percentage of blending for low-contrast objects [24; 29; 33; 39; 40]. The model-based Veo algorithm allows limiting these drawbacks by modeling the entire imaging system (i.e., X-ray tube output, detector response, radiation scatter properties, geometric features of the beam) [21; 22; 34; 41]. However, due to the complexity of the imaging system model, Veo reconstruction time is greatly increased compared with ASiR, hampering its wide use in clinical practice [31; 34; 41; 42]. The more recent ASiR-V algorithm has been proposed to reduce reconstruction time with respect to Veo while preserving some advantages of the full model-based approach in terms of image quality [22; 34; 36]. In particular, ASiR-V features more advanced noise and object modeling as compared with ASiR [36] and implements parts of the Veo physics model, but excludes the system optics in the modeling process, enabling reconstruction time almost comparable to that of FBP [36]. Like ASiR, ASiR-V can be blended with FBP at different percentages of reconstruction, from 0% (i.e., FBP) to 100% (i.e., pure ASiR-V) [36]. Several previous studies have characterized the image quality of CT imaging with ASiR [24; 28; 29; 33; 43; 44], finding substantial noise reduction properties as compared with conventional FBP. For instance, [PERSON] et al. showed that the benefit of ASiR in terms of noise reduction occurs almost independently of dose level (CTDI\({}_{\text{vol}}\) range: 0.2-7.2 mGy) with a noise reduction relative to FBP of approximately 20% and 50% for ASiR 40% and ASiR 100%, respectively [24]. Analogously, [PERSON] et al. found, for two different radiation exposures (tube loads of 130 and 260 mAs at 120 kVp), a noise reduction relative to FBP of approximately 30% and 50% with ASiR 50% and ASiR 100%, respectively [43]. A study by [PERSON] et al. confirmed that ASiR has the capability of reducing noise by up to 50% relative to FBP while preserving diagnostic information at low radiation exposure [33]. More recently, a number of clinical and phantom studies have evaluated the dose reduction potential and performance of ASiR-V [45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59]. In particular, some studies have assessed the noise properties of CT imaging with ASiR-V in comparison with FBP or other IR algorithms [45; 46; 49; 50; 51; 52; 53; 54; 57; 58; 59]. For instance, a clinical study by [PERSON] et al. showed that a relevant noise reduction can be obtained using ASiR-V 40% with respect to ASiR 50% in the CT imaging of oncologic patients [50]. [PERSON] et al. compared abdomen and phantom CT images reconstructed by adopting FBP, ASiR, Veo, and ASiR-V algorithms [51]. They performed subjective and objective analyses showing that ASiR-V 30% and ASiR-V 60% provided the best combination of qualitative and quantitative performances. In a phantom study, [PERSON] et al. compared the image quality of images reconstructed using FBP, ASiR, and ASiR-V algorithms [52]. They found that ASiR-V can allow an appreciable reduction of CT image noise and an improvement in contrast-to-noise ratio and spatial resolution. [PERSON] et al. evaluated image noise, spatial resolution, lesion detectability, and dose reduction potential of ASiR-V (50% and 100%) in comparison with FBP by performing phantom CT acquisitions [54]. They reported that ASiR-V has the capability of reducing image noise and improving low-contrast lesion detectability when compared with FBP. They suggested that such improvement in detection can allow radiation exposure reduction.