  Rapid and accurate diagnosis of pneumothorax, utilizing chest X-ray and
computed tomography (CT), is crucial for assisted diagnosis. Chest X-ray is
commonly used for initial localization of pneumothorax, while CT ensures
accurate quantification. However, CT scans involve high radiation doses and can
be costly. To achieve precise quantitative diagnosis while minimizing radiation
exposure, we proposed X-Recon, a CT ultra-sparse reconstruction network based
on ortho-lateral chest X-ray images. X-Recon integrates generative adversarial
networks (GANs), including a generator with a multi-scale fusion rendering
module and a discriminator enhanced by 3D coordinate convolutional layers,
designed to facilitate CT reconstruction. To improve precision, a projective
spatial transformer is utilized to incorporate multi-angle projection loss.
Additionally, we proposed PTX-Seg, a zero-shot pneumothorax segmentation
algorithm, combining image processing techniques with deep-learning models for
the segmentation of air-accumulated regions and lung structures. Experiments on
a large-scale dataset demonstrate its superiority over existing approaches.
X-Recon achieved a significantly higher reconstruction resolution with a higher
average spatial resolution and a lower average slice thickness. The
reconstruction metrics achieved state-of-the-art performance in terms of
several metrics including peak signal-to-noise ratio. The zero-shot
segmentation algorithm, PTX-Seg, also demonstrated high segmentation precision
for the air-accumulated region, the left lung, and the right lung. Moreover,
the consistency analysis for the pneumothorax chest occupancy ratio between
reconstructed CT and original CT obtained a high correlation coefficient. Code
will be available at: https://github.com/wangyunpengbio/X-Recon
