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A generative adversarial network model for improved three-dimensional mapping of pulmonary arteries and veins from non-contrast computed tomography in sublobar resection planning

   
@article{QIMS155123,
	author = {Hua Zhong and Anqi Li and Yichen Zhan and Jianbing He and Wenxue Wu and Dantong Zhang and Mingya Zhang and Ziying Lin and Guoxiang Cai},
	title = {A generative adversarial network model for improved three-dimensional mapping of pulmonary arteries and veins from non-contrast computed tomography in sublobar resection planning},
	journal = {Quantitative Imaging in Medicine and Surgery},
	volume = {16},
	number = {7},
	year = {2026},
	keywords = {},
	abstract = {Background: Preoperative three-dimensional (3D) vascular reconstruction from non-contrast computed tomography (NCCT) is often hindered by limited vessel differentiation, whereas contrast-enhanced computed tomography (CECT) is contraindicated for certain patients. This study examined the use of a generative adversarial network (GAN) for synthetic CECT (Syn-CECT)-like images from NCCT, preliminarily evaluating its clinical potential to assist in perinodular vascular segmentation and 3D reconstruction.Methods: This retrospective study included 212 patients. To mitigate the spatial misalignments caused by respiratory motion, the training phase (130 patients) included paired virtual non-contrast (VNC) and true CECT images derived from spectral computed tomography (CT). A Pix2pixGAN framework integrating an attention mechanism and a 2.5-dimensional (2.5D) strategy was applied to synthesize Syn-CECT. The model was then validated on external test sets (82 patients) from two different CT vendors. A quantitative evaluation was conducted via objective image quality metrics. The vascular reconstruction performance within a 50-mm spherical region around pulmonary nodules was quantitatively assessed according to the local Dice similarity coefficient (LocalDice), fragmentation index (FragIndex), and 95th percentile Hausdorff distance (HD95), alongside subjective physician scoring.Results: In external testing, Syn-CECT showed favorable structural compared to original NCCT as indicated by the multiscale structural similarity index measurement. For the downstream 3D vascular segmentation task, Syn-CECT yielded measurable improvements over NCCT, reflected by a moderately higher LocalDice (0.89–0.90 vs. 0.85–0.89), a lower FragIndex (3.82–5.92 vs. 4.54–8.68), and a lower HD95 (0.08 vs. 0.09–0.12) (all P values },
	issn = {2223-4306},	url = {https://qims.amegroups.org/article/view/155123}
}