Estimating Total Lung Volume from Pixel-Level Thickness Maps of Chest Radiographs Using Deep Learning

Purpose: To estimate the total lung volume (TLV) from real and synthetic frontal chest radiographs on a pixel level using lung thickness maps generated by a U-Net deep learning model. Materials and Methods: This retrospective study included 5959 chest CT scans from two public datasets, the Lung Nodule Analysis 2016 (Luna16) (n = 656) and the Radiological Society of North America Pulmonary Embolism Detection Challenge 2020 (n = 5303). Additionally, 72 participants were selected from the Klinikum Rechts der Isar dataset (October 2018 through December 2019), each with a corresponding chest radiograph obtained within 7 days. Synthetic radiographs and lung thickness maps were generated using forward projection of CT scans and their lung segmentations. A U-Net model was trained on synthetic radiographs to predict lung thickness maps and estimate TLV. Model performance was assessed using mean squared error (MSE), Pearson correlation coefficient, and two-sided Student t distribution. Results: The study included 72 participants (45 male and 27 female participants; 33 healthy participants: mean age, 62 years [range, 34–80 years]; 39 with chronic obstructive pulmonary disease: mean age, 69 years [range, 47–91 years]). TLV predictions showed low error rates (MSE_{Public−Synthetic}, 0.16 L²; MSE_{KRI−Synthetic}, 0.20 L²; MSE_KRI−Real, 0.35 L²) and strong correlations with CT-derived reference standard TLV (n_{Public−Synthetic}, 1191; r = 0.99; P < .001) (n^{, 72;r= 0.97;P< .001) (n}_KRI−, 72; r = 0.91; P < .001). When evaluated on different datasets, the U-Net model achieved the highest performance Synthetic KRI−Real for TLV estimation on the Luna16 test dataset, with the lowest MSE (0.09 L²) and strongest correlation (r = 0.99; P < .001) compared with CT-derived TLV. Conclusion: The U-Net–generated pixel-level lung thickness maps successfully estimated TLV for both synthetic and real radiographs.