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Updated: Jan 22, 2026
Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
Published on: May 19, 2023
Comparison of conventional 512-matrix CT images with Swin2SR-based 2048-matrix images in the visualization and
Yaping Zhang1, Ai Wang2, Qingyao Li3
1Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd. 100, Shanghai, 200080, China.
Purpose:
Clear visualization and diagnosis of lung nodules depend on the spatial resolution of CT images. Transformer-based generative neural networks can generate super-resolution images. To compare the diagnostic value of standard CT images of 512 × 512 pixels and Swin2SR-based super-resolution images of 2048 × 2048 pixels for lung cancer.
Materials And Methods:
The transformer-based Swin2SR model, which can upscale standard 512 × 512 pixel CT images to 2048 × 2048 super-resolution images, was validated with four retrospective datasets, three of which were patient data at three hospitals from January 2018 to December 2020, and another was the public Non-Small Cell Lung Cancer-Radiogenomics dataset. Lung nodules < 3 cm were included to validate the image quality of super-resolution images, and to compare the ability of standard and super-resolution images to display malignancy-associated imaging features and to diagnose lung cancer.
Results:
1161 nodules (663 malignant and 498 non-malignant) in 1161 subjects (age 60.2 ± 9.9 years, 653 males [56.2%]) were included. Swin2SR-based super-resolution images of these nodules had higher image scores (image quality, sharpness and noise) than standard images (p < 0.001). Among the malignancy-associated imaging features, the Swin2SR-based super-resolution images showed significantly more bubble-like lucency and air bronchogram than standard images (p < 0.001). Of the 663 histologically confirmed malignant nodules, 577 (87.0%) were considered malignant on Swin2SR-based super-resolution images, which was significantly higher than the 529 (79.8%) nodules on standard images (P = 0.037). The accuracy of Swin2SR-based super-resolution images and standard images in diagnosing lung cancer was 0.83 (95% CI 0.81-0.85) and 0.79 (0.76-0.81), respectively.
Conclusion:
Swin2SR-based super-resolution 2048 × 2048 pixel CT images can clearly show the malignancy-associated imaging features of lung nodules and improve the diagnostic of lung cancer.
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