This study used a sample of 131 participants who underwent low-dose computed tomography (LDCT) and standard-dose computed tomography (SDCT) to determine the effect of dose reduction and kernel selection on quantifying emphysema. The authors determined that the deep learning-based CT kernel conversation of sharp kernel in LDCT significantly reduced the variation in emphysema quantification. Key points Low-dose computed tomography with smooth kernel showed adequate performance in quantifying emphysema compared with standard-dose CT. Emphysema quantification is affected by kernel selection and the application of a sharp kernel resulted in a significant overestimation of emphysema. Deep learning-based kernel normalization of sharp kernel significantly reduced variation in emphysema quantification. Article: Emphysema quantification using low-dose computed tomography with deep learning-based kernel conversion comparison Authors: So Hyeon Bak, Jong Hyo Kim, Hyeongmin Jin, Sung Ok Kwon, Bom Kim, Yoon Ki Cha & Woo Jin Kim
Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations
Deep‑learning reconstruction (DLR) shifts CT image formation from a hardware‑limited process to a data‑driven one. In our real‑world cohort of >10,000 body scans, we observed a
28. April 2025
