Estimations of lung structural properties from a single propagation-based dark-field X-ray image
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| author | O'Connell, Dylan W. Morgan, Kaye S. Croton, Linda C. P. Pollock, James A. Ruben, Gary Crossley, Kelly J. Wallace, Megan J. McGillick, Erin. V. Kitchen, Stuart B. Hooper. Marcus J. |
| author_facet | O'Connell, Dylan W. Morgan, Kaye S. Croton, Linda C. P. Pollock, James A. Ruben, Gary Crossley, Kelly J. Wallace, Megan J. McGillick, Erin. V. Kitchen, Stuart B. Hooper. Marcus J. |
| contents | In this investigation, we applied a single-projection dark-field imaging technique to gain statistical information on the smallest airway structures within the lung$\unicode{x2014}$the alveoli$\unicode{x2014}$focusing on their size and number as key indicators of lung health. The algorithm employed here retrieves the projected thickness of the sample from a propagation-based phase contrast image using the transport-of-intensity equation. The first Born approximation is then used to isolate the dark-field signal associated with edge scattering, which increases the visibility of microstructure boundaries. PMMA spheres of known sizes were imaged first as an idealised alveolar model. The dark-field signal was then recovered from propagation-based phase-contrast X-ray images of the lungs of small mammals using this method. The retrieved dark-field signal was found to be proportional to both the alveolar size ($R^2 = 0.85$) and the number in projection ($R^2 = 0.69$), and these measurements could be combined to provide an estimate of the total surface area of the alveolar interfaces ($R^2 = 0.78$). This demonstrates the approach's ability to indicate lung health using the dark-field signal retrieved from a single-phase-contrast X-ray image. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_02695 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Estimations of lung structural properties from a single propagation-based dark-field X-ray image O'Connell, Dylan W. Morgan, Kaye S. Croton, Linda C. P. Pollock, James A. Ruben, Gary Crossley, Kelly J. Wallace, Megan J. McGillick, Erin. V. Kitchen, Stuart B. Hooper. Marcus J. Medical Physics Optics In this investigation, we applied a single-projection dark-field imaging technique to gain statistical information on the smallest airway structures within the lung$\unicode{x2014}$the alveoli$\unicode{x2014}$focusing on their size and number as key indicators of lung health. The algorithm employed here retrieves the projected thickness of the sample from a propagation-based phase contrast image using the transport-of-intensity equation. The first Born approximation is then used to isolate the dark-field signal associated with edge scattering, which increases the visibility of microstructure boundaries. PMMA spheres of known sizes were imaged first as an idealised alveolar model. The dark-field signal was then recovered from propagation-based phase-contrast X-ray images of the lungs of small mammals using this method. The retrieved dark-field signal was found to be proportional to both the alveolar size ($R^2 = 0.85$) and the number in projection ($R^2 = 0.69$), and these measurements could be combined to provide an estimate of the total surface area of the alveolar interfaces ($R^2 = 0.78$). This demonstrates the approach's ability to indicate lung health using the dark-field signal retrieved from a single-phase-contrast X-ray image. |
| title | Estimations of lung structural properties from a single propagation-based dark-field X-ray image |
| topic | Medical Physics Optics |
| url | https://arxiv.org/abs/2505.02695 |