CT Image Quality Evaluation Using Deep Learning Image Reconstruction Algorithm - Moon, Young-Ju; Lim, Woo-Taek; Lim, Chung-Hwan; Jung, Hong-Ryang (International Journal of Intelligent Systems and Applications in Engineering, 2023)
This this study analyzed retrospectively reconstructed images by applying the deep learning image reconstruction (DLIR) algorithm from dynamic liver CT scan images, to find the optimal DLIR algorithm that can improve image quality compared to ASIR-V while maintaining low radiation dose. Our hospital used the method of reconstructing images of 30 patients who have undergone dynamic liver CT scan retrospectively. The quality of the images of ASIR-V 30% were then compared to the quality of images reconstructed with DLIR-high and DLIR-medium techniques. The DLIR technique improved the signa-to-noise ratio (SNR) by increasing the image signal and reducing the noise compared to ASIR. In contrast-to-noise ratio (CNR) measurement results, the DLIR-high had the highest values, followed by DLIR-medium and ASIR, although their differences were not statistically significant. However, when comparing the value of CNR comprehensively, it showed statistically significant difference in order of ASIR > DLIR-medium > DLIR-high algorithms. Results showed that DLIR improved SNR and CNR by reducing the noise without image distortion. However, since the stronger DLIR intensity, the more the image blurring, it is significant to find the appropriate value of DLIR strength. In conclusion, DLIR showed results of improving SNR and CNR by reducing the noise without causing image distortion. However, since the stronger DLIR intensity, the more the image blurring is increased, it is significant to find the appropriate value for DLIR strength.