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Purpose: To improve the image quality of sparse-view computed tomography (CT)
images with a U-Net for lung cancer detection and to determine the best
trade-off between number of views, image quality, and diagnostic confidence.
Methods: CT images from 41 subjects (34 with lung cancer, seven healthy) were
retrospectively selected (01.2016-12.2018) and forward projected onto 2048-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, respectively. 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, seven healthy) for a single-blinded reader study. The
selected slices, for all levels of subsampling, with and without
post-processing by the U-Net model, were presented to three readers. Image
quality and diagnostic confidence were ranked using pre-defined scales.
Subjective nodule segmentation was evaluated utilizing sensitivity (Se) and
Dice Similarity Coefficient (DSC) with 95% confidence intervals (CI).
Results: The 64-projection sparse-view images resulted in Se = 0.89 and DSC =
0.81 [0.75,0.86] while their counterparts, post-processed with the U-Net, had
improved metrics (Se = 0.94, DSC = 0.85 [0.82,0.87]). Fewer views lead to
insufficient quality for diagnostic purposes. For increased views, no
substantial discrepancies were noted between the sparse-view and post-processed
images.
Conclusion: Projection views can be reduced from 2048 to 64 while maintaining
image quality and the confidence of the radiologists on a satisfactory level.
