Value of deep learning reconstruction of chest low-dose CT for image quality improvement and lung parenchyma assessment on lung window

Influence of deep learning image reconstruction algorithm for reducing radiation dose and image noise compared to iterative reconstruction and filtered back projection for head and chest computed tomography examinations: a systematic review

“… 31 Wang et al noted LDCT with DLIR provides higher scores for assessing pulmonary lesions except for sub-solid nodules or ground glass opacity nodules (GGN) compared to SD with HIR, whereas GGN greater than 4 mm can be picked up on LDCT DLIR images. 38 Tian et al reported that DLIR-H appeared to be slightly smoothed and DLIR M provides higher structures on visualization of smoother structures. 36 Ferri et al reported DLIR reconstruction series provided the smallest volume of emphysema compared with Adaptive statistical iterative reconstruction-V (ASIR-V) and FBP and also observed the increase in strength of DLIR led to a decrease in the size of emphysema.…”

“…29 CT chest examination: A total of nine studies from chest CT that used DLIR showed a reduction in IN (9-50%) compared with IR and FBP. [30][31][32][33][34][35][36][37][38][39] Four studies compared LD with DLIR and SD with IR for chest CT and observed a reduction (62-97%) in RD. 30,32,34,35 One study done with Quarter low dose (QLD) CT Chest using DLIR showed 75% reduction in radiation dose compared to IR.…”