Abstract
Abstract
Background
The low absorption of x-rays in lung tissue and the poor resolution of conventional computed tomography (CT) limits its use to detect lung disease. However, x-ray dark-field imaging can sense the scattered x-rays deflected by the structures being imaged. This technique can facilitate the detection of small alveolar lesions that would be difficult to detect with conventional CT. Therefore, it may provide an alternative imaging modality to diagnose lung disease at an early stage.
Methods
Eight mice were inoculated with lung cancers simultaneously. Each time two mice were scanned using a grating-based dark-field CT on days 4, 8, 12, and 16 after the introduction of the cancer cells. The detectability index was calculated between nodules and healthy parenchyma for both attenuation and dark-field modalities. High-resolution micro-CT and pathological examinations were used to crosscheck and validate our results. Paired t-test was used for comparing the ability of dark-field and attenuation modalities in pulmonary nodule detection.
Results
The nodules were shown as a signal decrease in the dark-field modality and a signal increase in the attenuation modality. The number of nodules increased from day 8 to day 16, indicating disease progression. The detectability indices of dark-field modality were higher than those of attenuation modality (p = 0.025).
Conclusions
Compared with the standard attenuation CT, the dark-field CT improved the detection of lung nodules.
Relevance statement
Dark-field CT has a higher detectability index than conventional attenuation CT in lung nodule detection. This technique could improve the early diagnosis of lung cancer.
Key points
• Lung cancer progression was observed using x-ray dark-field CT.
• Dark-field modality complements with attenuation modality in lung nodule detection.
• Dark-field modality showed a detectability index higher than that attenuation in nodule detection.
Graphical Abstract
Funder
National Natural Science Foundation of China
Tsinghua Precision Medicine Foundation
Tsinghua University
Publisher
Springer Science and Business Media LLC
Reference48 articles.
1. The top 10 causes of death. https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death. Accessed 29 Dec 2022
2. Siegel RL, Miller KD, Fuchs HE, Jemal A (2022) Cancer statistics, 2022. CA Cancer J Clin 72:7–33. https://doi.org/10.3322/caac.21708
3. Torre LA, Siegel RL, Jemal A (2016) Lung cancer statistics. In: Ahmad A, Gadgeel S (eds) Lung cancer and personalized medicine: current knowledge and therapies. Springer International Publishing, Cham, pp 1–19
4. Quaderi SA, Hurst JR (2018) The unmet global burden of COPD. Glob Health Epidemiol Genomics 3:e4. https://doi.org/10.1017/gheg.2018.1
5. Sullivan J, Pravosud V, Mannino DM, Siegel K, Choate R, Sullivan T (2018) National and state estimates of COPD morbidity and mortality — United States, 2014-2015. Chronic Obstr Pulm Dis 5:324–333. https://doi.org/10.15326/jcopdf.5.4.2018.0157
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Revolutionizing Medical Diagnostics;Advances in Medical Technologies and Clinical Practice;2024-04-15