MDCT에서 선량 변화에 따른 딥러닝 재구성 기법의 유용성 연구

• Published in: 한국방사선학회 논문지 Vol. 17; no. 1; pp. 37 - 46
• Main Authors: 김가현, Ga-hyun Kim, 김지수, Ji-soo Kim, 김찬들, Chan-deul Kim, 이준표, Joon-pyo Lee, 홍주완, Joo-wan Hong, 한동균, Dong-kyoon Han
• Format: Journal Article
• Language: Korean
• Published: 한국방사선학회 28.02.2023
• Subjects: ASIR-V; FBP; TrueFidelity; 딥러닝 재구성 기법; 적응형 통계적 재구성 기법; 필터보정역투영법
• ISSN: 1976-0620

This study aims to evaluate the usefulness of Deep Learning Image Reconstruction (TrueFidelity, TF), the image quality of existing Filtered Back Projection (FBP) and Adaptive Statistical Iterative Reconstruction-Veo (ASIR-V) were compared. Noise, CNR, and SSIM were measured by obtaining images with doses fixed at 17.29 mGy and altered to 10.37 mGy, 12.10 mGy, 13.83 mGy, and 15.56 mGy in reconstruction techniques of FBP, ASIR-V 50%, and TF-H. TF-H has superior image quality compared to FBP and ASIR-V when the reconstruction technique change is given at 17.29 mGy. When dose changes were made, Noise, CNR, and SSIM were significantly different when comparing 10.37 mGy TF-H and FBP (p< 0.05), and no significant difference when comparing 10.37 mGy TF-H and ASIR-V 50% (p >0.05). TF-H has a dose-reduction effect of 30%, as the highest dose of 15.56 mGy ASIR-V has the same image quality as the lowest dose of 10.37 mGy TF-H. Thus, Deep Learning Reconstruction techniques (TF) were able to reduce dose compared to Iterative Reconstruction techniques (ASIR-V) and Filtered Back Projection (FBP). Therefore, it is considered to reduce the exposure dose of patients.