Assessment of Inflation in a Human Cadaveric Lung with Dark-Field Chest Radiography

Published Online:https://doi.org/10.1148/ryct.220093

Abstract

Summary

Dark-field chest radiography signal intensity appeared to correlate with inflation status in a cadaveric lung.

Introduction

Dark-field radiography is an emerging technique for the imaging of pulmonary diseases, both in animal studies and humans (1). Signal is generated by small-angle scattering of x-rays due to multiple refractions at tissue-air interfaces in lung alveoli. First studies in humans postulate that the quantitative dark-field signal of the lung correlates with the number of tissue-air interfaces in lung tissue (2,3). However, dark-field signal has been analyzed only at full inspiration in humans. In intensive care medicine, choice of mechanical ventilation pressure can affect patient survival. Particularly in patients with acute respiratory distress syndrome (ARDS), it is important to both improve lung function and decrease the chance of developing ventilator-induced lung injury (4). Thus, a marker indicating the condition of lung alveoli at different pressure levels is highly desirable.

This study aims to assess dark-field signal changes generated by the lung at different levels of inflation pressure in a human cadaver.

Materials and Methods

This study was approved by the institutional review board, with waiver of informed consent. Two days after death, a cadaver of a 78-year-old man (cause of death: cardiac arrest with unsuccessful resuscitation; underlying disease: osseous metastatic prostate cancer, no pulmonary metastasis) was intubated and inflated at different pressure levels (0.0, 1.0, 2.0, and 3.0 kPa, or 0.0, 0.15, 0.29, and 0.44 psi, respectively) in three cycles. At all pressure levels, dark-field imaging of the chest was performed with a prototype radiography system, simultaneously yielding both conventional attenuation and dark-field radiographs, as described previously (5). The entire thorax was scanned within 30 seconds, as the system operates at a scan speed of approximately 11 mm per second. The entire lung was segmented manually on both image types, and a region of interest (ROI) was placed in the right mid and lower zone to maximize the lung diameter through which the x-ray beam passes (Fig 1). Both ROIs and whole-lung segmentations included the ribs. For every ventilation pressure and cycle, the total signal of the analyzed area was calculated for both dark-field images and conventional images in meters squared, similar to a previous study (1). The dark-field signal was calculated via −ln (V/V0), where V is the sample and V0 the reference visibility, that is, the visibility from an empty scan (6). Correspondingly, the attenuation signal was calculated via −ln (I/I0), were I is the sample intensity and I0 the reference intensity. Signal intensity levels for both modalities were plotted for different ventilation settings and cycles using both approaches.

(A) Conventional and (B) dark-field radiographs obtained in a cadaver of a                     78-year-old man, with mechanical ventilation adjusted to 3.0 kPa (0.44 psi).                     Example square regions of interest in the right mid and lower zone (light blue                     and light green) and manually segmented lung masks (dark blue and dark green)                     are marked in the image.

Figure 1: (A) Conventional and (B) dark-field radiographs obtained in a cadaver of a 78-year-old man, with mechanical ventilation adjusted to 3.0 kPa (0.44 psi). Example square regions of interest in the right mid and lower zone (light blue and light green) and manually segmented lung masks (dark blue and dark green) are marked in the image.

In addition, CT imaging was performed at 0.0 kPa (0.0 psi) to assess postmortem changes.

Results

Postmortem CT revealed bilateral pleural effusion with adjacent consolidations and dystelectasis, with no abnormal findings in ventral lung segments (Fig 2).

(A, B) Axial and (C) coronal reformations of the postmortem CT scan show                     pleural effusion (*, predominantly on the right side) with adjacent                     consolidation and dystelectasis (arrow) after cardiac arrest and unsuccessful                     resuscitation. The ventrally located lung segments appear properly ventilated.                     Yellow lines indicate the positions of the axial reformations.

Figure 2: (A, B) Axial and (C) coronal reformations of the postmortem CT scan show pleural effusion (*, predominantly on the right side) with adjacent consolidation and dystelectasis (arrow) after cardiac arrest and unsuccessful resuscitation. The ventrally located lung segments appear properly ventilated. Yellow lines indicate the positions of the axial reformations.

With higher pressure, the segmented lung area had higher visual (Fig 3) and quantitative (Fig 4) signal intensity on both dark-field and attenuation images. Overall dark-field signal intensities at the same pressure were higher at later cycles (Fig 4B). The respective overall attenuation signal did not appear to follow any specific pattern (Fig 4A). ROI dark-field signal intensity was lower when pressure changed from 0.0 kPa to 1.0 kPa (0.00 psi to 0.15 psi); thereafter, it followed the same pattern as that of the entire lung (Fig 4D). The attenuation signal of the ROI behaved opposite to the dark-field signal, with a lower signal at higher pressures and later cycles (Fig 4C).

Conventional (upper row) and dark-field (lower row) radiographs of the                     first inflation cycle at (A, E) 0.0 kPa (0.0 psi), (B, F) 1.0 kPa (0.15 psi),                     (C, G) 2.0 kPa (0.29 psi), and (D, H) 3.0 kPa (0.44 psi). For both imaging                     modalities, greater lung volume can be observed with higher pressure.                     Attenuation signal of the lung visually decreases with higher pressure levels,                     while dark-field signal increases.

Figure 3: Conventional (upper row) and dark-field (lower row) radiographs of the first inflation cycle at (A, E) 0.0 kPa (0.0 psi), (B, F) 1.0 kPa (0.15 psi), (C, G) 2.0 kPa (0.29 psi), and (D, H) 3.0 kPa (0.44 psi). For both imaging modalities, greater lung volume can be observed with higher pressure. Attenuation signal of the lung visually decreases with higher pressure levels, while dark-field signal increases.

(A, C) Attenuation and (B, D) dark-field signal calculated from (A, B) the                     entire lung and from (C, D) a square region of interest (ROI) only. Dark-field                     signal for both approaches follows the same specific pattern, with higher signal                     at higher pressure and later cycles (exception: square ROI, first cycle from 0.0                     kPa [0.0 psi] to 1.0 kPa [0.15 psi]) (B, D). Attenuation signal of the square                     ROI behaves the opposite way (C). Attenuation signal over the entire lung does                     not appear to follow any specific pattern (A).

Figure 4: (A, C) Attenuation and (B, D) dark-field signal calculated from (A, B) the entire lung and from (C, D) a square region of interest (ROI) only. Dark-field signal for both approaches follows the same specific pattern, with higher signal at higher pressure and later cycles (exception: square ROI, first cycle from 0.0 kPa [0.0 psi] to 1.0 kPa [0.15 psi]) (B, D). Attenuation signal of the square ROI behaves the opposite way (C). Attenuation signal over the entire lung does not appear to follow any specific pattern (A).

Discussion

Dark-field radiography in a cadaveric lung showed signal changes in structurally unimpaired alveoli following mechanical inflation at different pressures, demonstrating that the dark-field signal is generated by tissue-air interfaces, in accordance with previous studies (13,7,8). Mechanical inflation of the cadaveric lung presumably reopens certain alveoli that had collapsed due to postmortem changes. Those alveoli generate additional dark-field signal, leading to an overall increased signal intensity in the lung. Consistently higher dark-field signal intensity at the same pressure at a later cycle might indicate that repetitive inflation increases the number of opened alveoli. Attenuation signal from the manual whole-lung segmentation showed no specific pattern, likely due to strong absorption of signal by the rib cage, especially at 70 kVp, that leads to bias in lung attenuation signals. The same applies for the surrounding soft tissue. As a result, general quantitative analysis of attenuation signal in conventional radiographs is highly sensitive to small differences in manual segmentation or different body shapes. One limitation of this study was that both the ROIs and lung segmentations included the ribs, which also generated some dark-field signal. While signals from the lung tissue and ribs cannot be differentiated, we assumed that signal from the ribs would remain constant with increasing inflation pressure, while changes in dark-field signal would originate from the lungs and a higher number of ventilated alveoli. While this study was performed in a single human cadaver, it may be an important step toward the analysis of dark-field signal in living humans at different inspiration levels. Our study particularly shows that the technique might be used in ventilated patients and patients with ARDS, as it allows for evaluation of the pulmonary ventilation state and potential alveolar recruiting. Furthermore, a study demonstrated that grating-based dark-field radiography substantially improved the visualization of lung injury induced by mechanical ventilation in a mouse model (9). Thus, the method may be applied to patients with COVID-19, as they often require prolonged mechanical ventilation and are prone to developing ARDS (10).

In conclusion, dark-field radiography allowed for quantitative analysis of the inflation cycle in a human cadaver.

Disclosures of conflicts of interest: F.T.G. No relevant relationships. M.F. No relevant relationships. F.D.M. No relevant relationships. K.W. No relevant relationships. T.U. No relevant relationships. J.H. No relevant relationships. A.A.F. No relevant relationships. A.P.S. No relevant relationships. M.R.M. No relevant relationships. F.K. No relevant relationships. F.F. No relevant relationships. C.B. No relevant relationships. F.P. No relevant relationships. D.P. No relevant relationships.

Author Contributions

Author contributions: Guarantors of integrity of entire study, F.T.G., J.H., A.A.F., F.P.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; agrees to ensure any questions related to the work are appropriately resolved, all authors; literature research, F.T.G., M.F., T.U., A.A.F., F.P., D.P.; clinical studies, F.T.G., F.D.M., K.W., T.U. J.H., A.A.F., A.P.S., F.K., F.F., C.B., F.P., D.P.; statistical analysis, M.F., K.W.; and manuscript editing, F.T.G., M.F., T.U., J.H., A.P.S., M.R.M., F.K., C.B., F.P., D.P.

Keywords: Thorax, Physics

* F.T.G. and M.F. contributed equally to this work.

1 Current address: Department of Physics, University of Trieste, Trieste, Italy.

Supported by the European Research Council (AdG 695045), the Federal Ministry of Education and Research (BMBF), and the Free State of Bavaria under the Excellence Strategy of the Federal Government and the Länder, the German Research Foundation (GRK2274), as well as by the Technical University of Munich–Institute for Advanced Study and the Karlsruhe Nano Micro Facility (KNMF, www.kit.edu/knmf), a Helmholtz Research Infrastructure at Karlsruhe Institute of Technology (KIT).

References

  • 1. Gassert FT, Urban T, Frank M, et al. X-ray dark-field chest imaging: qualitative and quantitative results in healthy humans. Radiology 2021;301(2):389–395. LinkGoogle Scholar
  • 2. Willer K, Fingerle AA, Noichl W, et al. X-ray dark-field chest imaging for detection and quantification of emphysema in patients with chronic obstructive pulmonary disease: a diagnostic accuracy study. Lancet Digit Health 2021;3(11):e733–e744. Crossref, MedlineGoogle Scholar
  • 3. Urban T, Gassert FT, Frank M, et al. Qualitative and quantitative assessment of emphysema using dark-field chest radiography. Radiology 2022;303(1):119–127. LinkGoogle Scholar
  • 4. Slutsky AS, Ranieri VM. Ventilator-induced lung injury. N Engl J Med 2013;369(22):2126–2136. Crossref, MedlineGoogle Scholar
  • 5. Willer K, Fingerle AA, Gromann LB, et al. X-ray dark-field imaging of the human lung – A feasibility study on a deceased body. PLoS One 2018;13(9):e0204565. Crossref, MedlineGoogle Scholar
  • 6. Pfeiffer F, Bech M, Bunk O, et al. Hard-X-ray dark-field imaging using a grating interferometer. Nat Mater 2008;7(2):134–137. Crossref, MedlineGoogle Scholar
  • 7. Gradl R, Morgan KS, Dierolf M, et al. Dynamic in vivo chest x-ray dark-field imaging in mice. IEEE Trans Med Imaging 2019;38(2):649–656. Crossref, MedlineGoogle Scholar
  • 8. De Marco F, Willer K, Gromann LB, et al. Contrast-to-noise ratios and thickness-normalized, ventilation-dependent signal levels in dark-field and conventional in vivo thorax radiographs of two pigs. PLoS One 2019;14(6):e0217858. Crossref, MedlineGoogle Scholar
  • 9. Yaroshenko A, Pritzke T, Koschlig M, et al. Visualization of neonatal lung injury associated with mechanical ventilation using x-ray dark-field radiography. Sci Rep 2016;6(1):24269. Crossref, MedlineGoogle Scholar
  • 10. Yang X, Yu Y, Xu J, et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. Lancet Respir Med 2020;8(5):475–481. Crossref, MedlineGoogle Scholar

Article History

Received: May 6 2022
Revision requested: July 14 2022
Revision received: Sept 14 2022
Accepted: Nov 8 2022
Published online: Dec 15 2022