Generating Lung Ventilation Images with Virtual Non-contrast Images from Dual-Energy CT Scans Using Multi-task Conditional Generative Adversarial Networks

Sangyoon Lee; Changyong Choi; Jongjun Won; Mijeong Song; Hye Jeon Hwang; Jeong Min Song; Sang Min Lee; Namkug Kim

doi:10.1007/s10278-025-01812-z

Generating Lung Ventilation Images with Virtual Non-contrast Images from Dual-Energy CT Scans Using Multi-task Conditional Generative Adversarial Networks

J Imaging Inform Med. 2026 Jan 8. doi: 10.1007/s10278-025-01812-z. Online ahead of print.

Authors

Sangyoon Lee^#¹, Changyong Choi^#^{2

3}, Jongjun Won^{2

4}, Mijeong Song², Hye Jeon Hwang⁵, Jeong Min Song^{2

5}, Sang Min Lee^#⁶, Namkug Kim^#^{7

8}

Affiliations

¹ Department of Radiation Oncology, University of Minnesota Medical School, 420 Delaware Street SE, Minneapolis, MN, 55455, USA.
² Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea.
³ Department of Biomedical Engineering, BK 21 Project, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea.
⁴ Department of Biomedical Engineering, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea.
⁵ Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea.
⁶ Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea. asellion@hanmail.net.
⁷ Department of Biomedical Engineering, BK 21 Project, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea. namkugkim@gmail.com.
⁸ Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea. namkugkim@gmail.com.

^# Contributed equally.

PMID: 41507659
DOI: 10.1007/s10278-025-01812-z

Abstract

Regional lung ventilation imaging using xenon-enhanced dual-energy CT (Xe-DECT) offers valuable insight into obstructive pulmonary diseases but remains limited in clinical use due to technical and logistical constraints. In this study, a multi-task conditional generative adversarial network (GAN) was developed to generate deep learning-generated ventilation images (DL-Vent) from virtual non-contrast (VNC) images. A total of 269 scans from 177 patients with COPD or asthma-COPD overlap syndrome (ACOS) were used to train, validate, and test the model. The architecture was designed to simultaneously predict ventilation images and emphysema masks using paired inspiratory and expiratory VNC input images. DL-Vent demonstrated strong similarity to measured Xe-DECT ventilation images (Xe-Vent), with dice similarity coefficients of 0.56 for ventilation defects and 0.88 for ventilation regions. The ventilation defect percentages (VDP) of DL-Vent and Xe-Vent showed a high correlation (r_s = 0.82), and both were similarly correlated with pulmonary function test results, including FEV1 (p = 0.71). Radiologists rated DL-Vent images as "fair to good" (mean score 3.9/5) and reliably differentiated defect patterns between COPD and ACOS (Cramer's V = 0.41, p = 0.03). The proposed model provides a promising alternative for functional lung imaging without requiring xenon administration.

Keywords: Deep learning; Dual-energy CT; Generative adversarial networks; Lung ventilation; Pulmonary disease.

Abstract

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