Quantitative Analysis of Emphysema in Ultra-high-resolution CT by Using Deep Learning Reconstruction: Comparison with Hybrid Iterative Reconstruction

Abstract

The noise generated in ultra-high-resolution computed tomography (U-HRCT) images affects the quantitative analysis of emphysema. In this study, we compared the physical properties of reconstructed images for hybrid iterative reconstruction (HIR) and deep learning reconstruction (DLR), which are reconstruction methods for reducing image noise. Using clinical evaluation, we evaluated the correlation between low attenuation volume (LAV) % obtained by CT and forced expiratory volume in 1 s per forced vital capacity (FEV1/FVC) obtained by respiratory function tests. CT data obtained by HIR and DLR were used for analysis (matrix size: 1024´1024, slice thickness: 0.25 mm). The physical characteristics were evaluated for the modulation transfer function (MTF) and noise power spectrum (NPS). Display-field of view (D-FOV) was analyzed by varying between 300 mm and 400 mm. The clinical data evaluated the relationship between LAV% and FEV1/FVC by Spearman's correlation coefficient. The 10% MTFs were 1.3 cycles/mm (HIR) and 1.3 cycles/mm (DLR) at D-FOV 300 mm, and 1.2 cycles/mm (HIR) and 1.1 cycles/mm (DLR) at D-FOV 400 mm. The NPS had less noise in DLR than HIR in all frequency ranges. The correlation coefficients between LAV% and FEV1/FVC were 0.64 and 0.71, respectively, in HIR and DLR. There was no difference in the resolution characteristics of HIR and DLR. DLR had better noise characteristics than HIR. The correlation between LAV% measured by HIR and DLR and FEV1/FVC is equivalent. The noise characteristics of the DLR enable the reduction of exposure to emphysema quantitative analysis by CT.