Code to the paper: "Improving Image Quality of Sparse-view Lung Cancer CT Images with U-Net"
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Background: We aimed at improving image quality (IQ) of sparse-view computed tomography (CT) images using a U-Net for lung metastasis detection and determining the best tradeoff between number of views, IQ, and diagnostic confidence.
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Methods: CT images from 41 subjects aged 62.8 ± 10.6 years (mean ± standard deviation), 23 men, 34 with lung metastasis, 7 healthy, were retrospectively selected (2016–2018) and forward projected onto 2,048-view sinograms. Six corresponding sparse-view CT data subsets at varying levels of undersampling were reconstructed from sinograms using filtered backprojection with 16, 32, 64, 128, 256, and 512 views. A dual-frame U-Net was trained and evaluated for each subsampling level on 8,658 images from 22 diseased subjects. A representative image per scan was selected from 19 subjects (12 diseased, 7 healthy) for a single-blinded multireader study. These slices, for all levels of subsampling, with and without U-Net postprocessing, were presented to three readers. IQ and diagnostic confidence were ranked using predefined scales. Subjective nodule segmentation was evaluated using sensitivity and Dice similarity coefficient (DSC); clustered Wilcoxon signed-rank test was used.
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Results: The 64-projection sparse-view images resulted in 0.89 sensitivity and 0.81 DSC, while their counterparts, postprocessed with the U-Net, had improved metrics (0.94 sensitivity and 0.85 DSC) (p = 0.400). Fewer views led to insufficient IQ for diagnosis. For increased views, no substantial discrepancies were noted between sparse-view and postprocessed images.
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Conclusion: Projection views can be reduced from 2,048 to 64 while maintaining IQ and the confidence of the radiologists on a satisfactory level.
- python==3.8.10
- tensorflow==2.4.0
- astra==2.1.1
- pandas==1.3.4
- scipy==1.4.1
- Obtain data
- Sparse-sample data with 1_dataPrep_sparseSampling.ipynb
- Train network with 2_train.ipynb
- Test model and obtain predicted images with 3_test.ipynb
- Evaluate results of reader study with 4_evalResults_readerStudy.ipynb
Authored by:
- Annika Ries (annika97.ries@gmail.com)
- Tina Dorosti (tina.dorosti@tum.de)
If you use this code in a scientific publication, we would appreciate citations to the following paper:
Ries, A., Dorosti, T., Thalhammer, J. et al. Improving image quality of sparse-view lung tumor CT images with U-Net. Eur Radiol Exp 8, 54 (2024). https://doi.org/10.1186/s41747-024-00450-4