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Ries A, Dorosti T, Thalhammer J, Sasse D, Sauter A, Meurer F, Benne A, Lasser T, Pfeiffer F, Schaff F, Pfeiffer D. Improving image quality of sparse-view lung tumor CT images with U-Net. Eur Radiol Exp 2024; 8:54. [PMID: 38698099 PMCID: PMC11065797 DOI: 10.1186/s41747-024-00450-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 02/09/2024] [Indexed: 05/05/2024] Open
Abstract
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.
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Affiliation(s)
- Annika Ries
- Chair of Biomedical Physics, Department of Physics, School of Natural Sciences, Technical University of Munich, Garching, 85748, Germany
- Munich Institute of Biomedical Engineering, Technical University of Munich, 85748, Garching, Germany
| | - Tina Dorosti
- Chair of Biomedical Physics, Department of Physics, School of Natural Sciences, Technical University of Munich, Garching, 85748, Germany.
- Munich Institute of Biomedical Engineering, Technical University of Munich, 85748, Garching, Germany.
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum Rechts Der Isar, Technical University of Munich, 81675, Munich, Germany.
| | - Johannes Thalhammer
- Chair of Biomedical Physics, Department of Physics, School of Natural Sciences, Technical University of Munich, Garching, 85748, Germany
- Munich Institute of Biomedical Engineering, Technical University of Munich, 85748, Garching, Germany
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum Rechts Der Isar, Technical University of Munich, 81675, Munich, Germany
- Institute for Advanced Study, Technical University of Munich, 85748, Garching, Germany
| | - Daniel Sasse
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum Rechts Der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Andreas Sauter
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum Rechts Der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Felix Meurer
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum Rechts Der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Ashley Benne
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum Rechts Der Isar, Technical University of Munich, 81675, Munich, Germany
- Institute for Advanced Study, Technical University of Munich, 85748, Garching, Germany
| | - Tobias Lasser
- Munich Institute of Biomedical Engineering, Technical University of Munich, 85748, Garching, Germany
- Computational Imaging and Inverse Problems, Department of Computer Science, School of Computation, Information, and Technology, Technical University of Munich, 85748, Garching, Germany
| | - Franz Pfeiffer
- Chair of Biomedical Physics, Department of Physics, School of Natural Sciences, Technical University of Munich, Garching, 85748, Germany
- Munich Institute of Biomedical Engineering, Technical University of Munich, 85748, Garching, Germany
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum Rechts Der Isar, Technical University of Munich, 81675, Munich, Germany
- Institute for Advanced Study, Technical University of Munich, 85748, Garching, Germany
| | - Florian Schaff
- Chair of Biomedical Physics, Department of Physics, School of Natural Sciences, Technical University of Munich, Garching, 85748, Germany
- Munich Institute of Biomedical Engineering, Technical University of Munich, 85748, Garching, Germany
| | - Daniela Pfeiffer
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum Rechts Der Isar, Technical University of Munich, 81675, Munich, Germany
- Institute for Advanced Study, Technical University of Munich, 85748, Garching, Germany
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