Real-time lung extraction from synthesized x-rays improves pulmonary image-guided radiotherapy.

Lung tumors can be obscured in X-rays, preventing accurate and robust localization. To improve lung conspicuity for image-guided procedures, we isolate the lungs in the anterior-posterior (AP) X-rays using a lung extraction network (LeX-net) that virtually removes overlapping thoracic structures, including ribs, diaphragm, liver, heart, and trachea. Approach: 73,965 thoracic 3DCTs and 106 thoracic 4DCTs were included. The 3D lung volume was extracted using an open-source lung volume segmentation model. AP digitally reconstructed radiographs (DRRs) of the full anatomy CT and extracted lungs were computed as the input and reference to train a network (LeX-net) to generate lung-extracted DRRs (LeX-net DRRs) from full anatomy DRRs, which adopted a Swin-UNet model with conditional GAN. Subsequently, the trained LeX-net on 3DCT was applied to 4DCT-derived DRRs. Lung tumor tracking was then performed on DRRs using a template-matching method on a holdoff 4DCT test set of 79 patients whose gross tumor volumes were smaller than 20 cm3. Main results: LeX-net successfully isolated the lungs in DRRs, achieving an SSIM of 0.9581 ± 0.0151 and a PSNR of 30.78 ± 2.50 on the testing set of 3DCT-derived DRRs. Its performance declined slightly when applied to the 4DCT but maintained useable lung-only 2D views. On the challenging test set including cases of organ overlap, high tumor mobility, and small tumor size, the individual tumor tracking error for LeX-net DRRs was 0.97 ± 0.86 mm, significantly lower than that of 3.13 ± 5.82 mm using the full anatomy DRRs. LeX-net improved success rates of using 5 mm, 3 mm, and 1 mm tracking windows from 88.1%, 80.0%, and 58.7% to 98.1%, 94.2%, and 73.8%, respectively. Significance: LeX-net removes overlapping anatomies and enhances visualization of the lungs in X-rays. The model trained using 3DCTs is generalizable to 4DCT-derived DRRs, achieving significantly improved tumor tracking outcome. .