Comparison of DLIR-H and ASIR algorithms for image reconstruction in low-dose chest CT of pediatric mycoplasma pneumoniae pneumonia: a cross-sectional study

Pediatric Mycoplasma pneumoniae pneumonia (MPP) accounts for a significant number of cases of community-acquired pneumonia, and chest computed tomography (CT) is critical for its early diagnosis and prognosis assessment. However, clinical utilization is restricted due to concerns regarding radiation exposure. Adaptive statistical iterative reconstruction (ASIR) has been extensively employed in low-dose pediatric CT but suffers from noise texture distortion. In contrast, deep learning image reconstruction (DLIR) has shown promise for enhancing image quality in adult CT. Nevertheless, the comparative effectiveness of DLIR and ASIR in low-dose chest CT for pediatric MPP remains unvalidated, presenting a significant knowledge gap that this study endeavours to bridge.