Assessment of Emphysema on X-ray Equivalent Dose Photon-Counting Detector CT Evaluation of Visual Scoring and Automated Quantification Algorithms

ObjectivesThe aim of this study was to evaluate the feasibility and efficacy of visual scoring, low-attenuation volume (LAV), and deep learning methods for estimating emphysema extent in x-ray dose photon-counting detector computed tomography (PCD-CT), aiming to explore future dose reduction potentials. MethodsOne hundred one prospectively enrolled patients underwent noncontrast low- and chest x-ray dose CT scans in the same study using PCD-CT. Overall image quality, sharpness, and noise, as well as visual emphysema pattern (no, trace, mild, moderate, confluent, and advanced destructive emphysema; as defined by the Fleischner Society), were independently assessed by 2 experienced radiologists for low- and x-ray dose images, followed by an expert consensus read. In the second step, automated emphysema quantification was performed using an established LAV algorithm with a threshold of -950 HU and a commercially available deep learning model for automated emphysema quantification. Automated estimations of emphysema extent were converted and compared with visual scoring ratings. ResultsX-ray dose scans exhibited a significantly lower computed tomography dose index than low-dose scans (low-dose: 0.66 +/- 0.16 mGy, x-ray dose: 0.11 +/- 0.03 mGy, P < 0.001). Interreader agreement between low- and x-ray dose for visual emphysema scoring was excellent (kappa = 0.83). Visual emphysema scoring consensus showed good agreement between low-dose and x-ray dose scans (kappa = 0.70), with significant and strong correlation (Spearman rho = 0.79). Although trace emphysema was underestimated in x-ray dose scans, there was no significant difference in the detection of higher-grade (mild to advanced destructive) emphysema (P = 0.125) between the 2 scan doses. Although predicted emphysema volumes on x-ray dose scans for the LAV method showed strong and the deep learning model excellent significant correlations with predictions on low-dose scans, both methods significantly overestimated emphysema volumes on lower quality scans (P < 0.001), with the deep learning model being more robust. Further, deep learning emphysema severity estimations showed higher agreement (kappa = 0.65) and correlation (Spearman rho = 0.64) with visual scoring for low-dose scans than LAV predictions (kappa = 0.48, Spearman rho = 0.45). ConclusionsThe severity of emphysema can be reliably estimated using visual scoring on CT scans performed with x-ray equivalent doses on a PCD-CT. A deep learning algorithm demonstrated good agreement and strong correlation with the visual scoring method on low-dose scans. However, both the deep learning and LAV algorithms overestimated emphysema extent on x-ray dose scans. Nonetheless, x-ray equivalent radiation dose scans may revolutionize the detection and monitoring of disease in chronic obstructive pulmonary disease patients.