Improving image quality of sparse-view lung tumor CT images with U-Net

• Published in: European radiology experimental Vol. 8; no. 1; pp. 54 - 11
• Main Authors: Ries, Annika, Dorosti, Tina, Thalhammer, Johannes, Sasse, Daniel, Sauter, Andreas, Meurer, Felix, Benne, Ashley, Lasser, Tobias, Pfeiffer, Franz, Schaff, Florian, Pfeiffer, Daniela
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 03.05.2024; Springer Nature B.V; SpringerOpen
• Subjects: Aged; Diagnostic Radiology; Female; Humans; Image processing (computer-assisted); Imaging; Internal Medicine; Interventional Radiology; Lung cancer; Lung metastasis; Lung Neoplasms - diagnostic imaging; Male; Medicine; Medicine & Public Health; Metastasis; Middle Aged; Neural networks (computer); Neuroradiology; Original Article; Radiographic Image Interpretation, Computer-Assisted - methods; Radiology; Retrospective Studies; Tomography, X-Ray Computed - methods; Tomography (x-ray computed); Ultrasound; Artifacts; Image processing (computer-assisted); Neural networks (computer); Tomography (x-ray computed); Lung metastasis
• ISSN: 2509-9280; 2509-9280
• DOI: 10.1186/s41747-024-00450-4

Background We aimed to improve the image quality (IQ) of sparse-view computed tomography (CT) images using a U-Net for lung metastasis detection and determine the best tradeoff between number of views, IQ, and diagnostic confidence. 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. 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. Conclusions Projection views can be reduced from 2,048 to 64 while maintaining IQ and the confidence of the radiologists on a satisfactory level. Relevance statement Our reader study demonstrates the benefit of U-Net postprocessing for regular CT screenings of patients with lung metastasis to increase the IQ and diagnostic confidence while reducing the dose. Key points • Sparse-projection-view streak artifacts reduce the quality and usability of sparse-view CT images. • U-Net-based postprocessing removes sparse-view artifacts while maintaining diagnostically accurate IQ. • Postprocessed sparse-view CTs drastically increase radiologists’ confidence in diagnosing lung metastasis. Graphical Abstract