Pulmonary emphysema quantification at low dose chest CT using Deep Learning image reconstruction

Abstract

PurposeQuantitative analysis of emphysema volume is affected by the radiation dose and the CT reconstruction technique. We aim to evaluate the influence of a commercially available deep learning image reconstruction algorithm (DLIR) on the quantification of pulmonary emphysema in low-dose chest CT. MethodsWe performed a retrospective study of low dose chest CT scans in 54 patients with chronic obstructive pulmonary disease (COPD). Raw data were reconstructed using FBP, iterative reconstruction (ASIR-V 70%) and deep learning based algorithms at high, medium and low-strength (DLIR -H, -M, -L). Filtered FBP images served as reference. Pulmonary emphysema volume (proportion of voxels below −950 UH) was measured on each reconstruction dataset and visually assessed by a chest radiologist. Quantitative image quality was assessed by placing 3 regions of interest in the trachea, in air and in a paraspinal muscle. Signal to noise ratio was also measured. ResultsThe mean CDTIvol was 2.38 ± 0.68 mGy. Significant differences in emphysema volumes between the filtered FBP reference and ASIR-V, DLIR-H, DLIR-M or DLIR-L were observed, (p < 10−3) for all. A strong correlation between filtered FBP volumes and DLIR-H was reported (r = 0.999, p < 10−4), a 10% overestimation with DLIR-H being observed. Noise was significantly reduced in DLIR-H volumes compared to the other reconstruction methods. Signal to noise ratio was improved when using DLIR-H (p < 10−6). ConclusionThere are significant differences regarding emphysema volumes between FBP, iterative reconstruction or deep learning-based DLIR algorithm. DLIR-H shows the closest correlation to filtered FBP while increasing SNR.