###### Abstract  
In low-dose cone beam computed tomography (CT), the insufficient number of photons inevitably results in noise, which reduces the accuracy of disease diagnosis. One approach to improving the image quality of CT images acquired using a low-dose protocol involves the utilization of a reconstruction algorithm that efficiently reduces noise. In this study, we modeled the Feldkamp-Davis-Kress (FDK) algorithm using various filters and projection angles and applied it to the reconstruction process using CT simulation. To quantitatively evaluate the quality of the reconstruction images, we measured the coefficient of variation (COV), and signal-to-noise ratio (SNR) in the air, brain, and bone regions to evaluate the noise level. Furthermore, we calculated root mean square error (RMSE), universal image quality index (UQI), and blind/referenceless image spatial quality evaluator (BRISQUE) as similarity and no-reference evaluation. The Hann filter of the FDK algorithm showed superior performance in terms of COV, SNR, RMSE, and UQI compared to the other filters. In addition, when analyzing the COV and SNR results, we observed that image quality increased significantly at projection angles smaller than approximately \(2.8^{\circ}\). Moreover, based on BRISQUE results, we confirm that the Shepp-Logan filter exhibited the most superior performance. In conclusion, we believe that the application of the Hann filter in the FDK reconstruction process offers significant advantages in improving the image quality acquired under a low-dose protocol, and we expect that our study will be a preliminary study of no-reference evaluation of CT reconstruction images.  
c 2024 14 7058 10.3390/app14167058 10.  
image quality because of the increase in X-ray quantum noise, which can decrease the accuracy of diagnosis [9].  
Therefore, to address these issues, many studies have been conducted to improve the quality of CBCT images acquired using a low-dose protocol [10; 11]. Among them, image quality improvement research based on CBCT reconstruction algorithms has been actively conducted, and various algorithms have been developed. One of the early proposed CBCT reconstruction algorithms, the Feldkamp-Davis-Kress (FDK) algorithm, operates based on statistical principles and has been widely used in the past decades. The FDK algorithm aims to estimate the true line integral from the CBCT by minimizing an objective function derived from the statistical properties of the noise in the projection data. The FDK algorithm, which reconstructs the image through filtering and back-projection, is used for most CBCT and has shown promising results in reducing noise in low-dose CBCT. The FDK algorithm can easily be implemented with currently available hardware and is widely used as an efficient method for producing practical images for various applications [12; 13; 14].  
Parameter selection in the reconstruction process of the FDK algorithm significantly influences the quality of the CBCT reconstruction image. Therefore, evaluating and analyzing the parameter selection and the resulting effects on image quality in reconstruction can significantly improve the quality of CBCT reconstruction images [15; 16]. Previous studies have evaluated the effects of various parameters in the FDK algorithm on the quality of CBCT reconstruction images and analyzed the characteristics and relationships between image noise and spatial resolution [17]. However, studies that include image similarity evaluation and no-reference evaluation according to changes in CBCT reconstruction parameters are lacking [18; 19]. Therefore, the purpose of this study was to evaluate the effect of the filter and projection angle on CBCT reconstruction image quality, and to quantitatively evaluate the image quality according to the parameters of FDK reconstruction by conducting image noise, similarity, and no-reference evaluations.