# New-Generation ASiR-V for Dose Reduction While Maintaining Image Quality in CT: A Phantom Study
## 1 Introduction
This is of particular relevance because the radiological community's greatest challenge has been to preserve the overall image quality, which has to be set at an adequate level since radiologists have to diagnose the clinical condition at the lowest achievable dose. To date, the general consensus is that image reconstruction algorithms have the potential to play an essential role in maintaining or improving image quality in reduced-dose CT. Among the several commercially available IR techniques, adaptive statistical iterative reconstruction (ASiR GE Healthcare, Waukesha, WI, USA) has been one of the most widely studied algorithms to date. This is a hybrid algorithm introduced in 2008 that uses the image information obtained from FBP as the basis for iterative reconstruction to optimize image quality. Through numerous iterations, ASiR models the noise present in the raw CT data and removes it to produce a clearer final image. To model the noise and object, ASiR offers dose reductions in the raw data domain ranging from 25% to 40%. [24]. However, quality artifacts, such as an artificial texture or a blotchy appearance, have often been associated with the use of ASiR, particularly when high-strength IR is used or a large number of patients are scanned, and radiologists are reluctant to accept images of such quality [21]. To overcome these limitations, a new generation of full model-based IR algorithms has been introduced in the market, providing the possibility to model the entire system, including physics and optics. However, these full model-based algorithms, such as the so-called VEO (\"I see\" in Spanish; the IR algorithm released by GE Healthcare after ASiR) have long processing times that are not practical and easy to implement in a radiological routine workflow [25]. VEO is a model-based iterative reconstruction product that generates high-clarity images with less than 1 mSv. VEO enables the generation of single images with lower noise and higher resolution. Simultaneously, significant dose reduction capabilities are available, presenting new possibilities for difficult cases and delicate patients. Recently, a new-generation adaptive statistical IR (ASiR-V, GE Healthcare, Waukesha, WI, USA) was introduced. ASiR-V is a hybrid between ASiR and VEO platforms and uses a nearly full IR process with the potential for clinically feasible dose reduction with better image quality than that of conventional ASiR, as well as a shorter image processing time [26]. The present study is focused on the performance characteristics of the ASiR-V algorithm (as compared with the ASiR approach) since it represents a commercially available promising technology for effective dose reduction along with the preservation of diagnostic image quality. ASiR-V, in contrast to ASiR, employs more sophisticated noise, object, and physics modeling. As opposed to VEO, ASiR-V employs a more straightforward system model for forward projection that places less emphasis on the system optics, leading to quicker reconstruction times (equivalent speed to that of FBP). Additionally, a further reduction in low-signal artifacts is possible with the new ASiR-V compared with earlier versions of ASiR. As a result, a meaningful improvement in image quality can be obtained without suffering a significant speed penalty during reconstruction. Ultimately, it is worth noting that for the ASiR algorithm, the optimal combination of blending percentage and NI is commonly defined in clinical practice. This, however, still remains an open issue for ASiR-V [27; 28; 29]. The aim of this study was to investigate whether the overall image quality of low-dose CT images, reconstructed with ASiR-V (GE Healthcare), is maintained when compared to CT images reconstructed with ASiR using routine radiation dose. Furthermore, we further investigated the optimal combination of NI and blending percentage for application in clinical practice using the ASiR-V algorithm.