## 4 Discussion  
Given that IR algorithms in CT imaging can be inherently nonlinear and nonstationary, noise properties should be possibly assessed by using a rigorous voxel-based approach exploiting multiple repeated acquisitions. Unlike the widely employed approach of estimating noise as the SD of CT numbers within an ROI, this more complex approach allows for solving potential issues related to nonvalidity of the ergodic theorem for CT imaging with IRs, as well as for assessing the spatial nonuniformity degree of noise. However, so far, only few studies [67, 68, 69, 70] have employed such an approach to characterize noise properties in CT imaging with IRs. In particular, [PERSON] and [PERSON] [68] performed multiple repeated CT acquisitions of a uniform phantom and of two anatomically informed textured phantoms, and used these phantoms to assess noise properties for FBP and the sinogram affirmed iterative reconstruction (SAFIRE, Siemens Healthcare) algorithm. They found that noise was globally nonstationary, and this was mostly relevant for SAFIRE-reconstructed CT images of textured phantoms. In a similar study, [PERSON] et al. [69] showed that the advanced modeled iterative reconstruction (ADMIRE, Siemens Healthcare) algorithm with different iterative strengths (i.e., 3, 4, and 5) allowed a noise reduction of up to 64% relative to FBP. A study by [PERSON] et al. [70] compared noise in CT images of a porcine liver phantom surrounded by agar gel acquired 100 times, reconstructed using FBP and the iterative model reconstruction (IMR, Philips Healthcare) algorithm with various noise reduction levels (L1, L2, and L3, where L3 offers the greatest noise reduction). They revealed a spatial dependence of noise for IMR-reconstructed CT images. Moreover, for IMR-L3, a noise reduction of 68%-75% relative to FBP was observed. [PERSON] et al. [67] acquired 50 repeated acquisitions of a homogeneous phantom in order to assess the spatial nonuniformity of noise maps associated with FBP- and Veo-reconstructed images. Veo-reconstructed CT images were characterized by at least a 50% reduction in _NUI_ values relative to FBP-reconstructed CT images. Some previous clinical or phantom studies have reported a higher noise reduction capability of ASiR-V as compared with ASiR. For instance, [PERSON] et al. [50] conducted a retrospective case-control study involving oncologic patients who underwent baseline and follow-up CT scans with ASiR 50% and ASiR-V 40%, respectively. Noise was estimated as the SD of CT numbers within three circular ROIs placed in homogeneous regions of three different tissues. Regardless of the anatomical region, noise was found lower in ASiR-V 40%-reconstructed images than in ASiR 50%-reconstructed images, albeit radiation exposure was 38% lower for the ASiR-V examinations than for the ASiR ones. Similarly, [PERSON] et al. carried out a retrospective study on 36 patients who underwent an abdomino-pelvic CT scan [51]. They assessed the noise level within lesions for CT images reconstructed using FBP, ASiR 80%, Veo, and ASiR-V (30%, 60%, and 90%). The highest noise level was revealed on FBP-reconstructed images, followed by ASiR-V 30%-, ASiR 80%-, Veo-, ASiR-V 60%-, and ASiR-V 90%-reconstructed images. [PERSON] et al. [52] compared the noise performances of ASiR-V, ASiR, and FBP on an anthropomorphic phantom using three blending levels of reconstruction (30%, 50%, and 70%) and five different tube currents. By assessing the noise magnitude and noise reduction relative to FBP within a circular ROI in a homogeneous area of the phantom, they found that ASiR-V 30%, ASiR-V 50%, and ASiR-V 70% can allow a noise reduction of 30%-45%, 41%-61%, and 52%-67%, respectively. On the other hand, ASiR with the same blending levels showed a lower noise reduction of 18%-20%, 30%-33%, and 41%-49%, respectively. [PERSON] et al. [54] characterized the noise level of ASiR-V-reconstructed (50% and 100% blending levels of reconstruction) CT images.