Skip to main content

Advertisement

Springer Nature Link
Log in

Lung water assessment by lung ultrasonography in intensive care: a pilot study

  • Original
  • Published:
Intensive Care Medicine Aims and scope Submit manuscript

Abstract

Objective

To investigate the accuracy of lung ultrasonography (LUS) in the quantification of lung water in critically ill patients by using quantitative computed tomography (CT) as the gold standard for the determination of lung weight.

Methods

Twenty consecutive patients admitted to an intensive care unit who underwent chest CT as a step in their clinical management were evaluated within 4 h by LUS. Lung weight, lung volume, and physical lung density were calculated from the CT scans using ad hoc software. Semiquantitative ultrasound assessment of lung water was performed by determining the ultrasound B-line score, defined as the total number of B-lines detectable in an anterolateral LUS examination.

Results

Good correlations were found between the B-line score and lung weight (r = 0.75, p < 0.05) and density (r = 0.82, p < 0.01), that only marginally increased when the lung density of the first 10 mm of subpleural lung tissue was evaluated (r = 0.83, p < 0.01). Moreover, values of subpleural lung density were not significantly different from values of the whole lung density (0.34 ± 0.11 vs. 0.37 ± 0.16 g/ml, p = ns). Very good correlations were found between the B-line score and both the weight (r = 0.85, p < 0.01) and the density (r = 0.88, p < 0.01) of the upper lobes. The weight of the lower lobes was not correlated with the B-line score (r = 0.14, p = ns).

Conclusions

Lung ultrasound B-lines are correlated with lung weight and density determined by CT. LUS may provide a reliable, simple and radiation-free lung densitometry in the intensive care setting.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Sakka SG, Klein M, Reinhart K, Meier-Hellmann A (2002) Prognostic value of extravascular lung water in critically ill patients. Chest 122:2080–2086

    Article  PubMed  Google Scholar 

  2. Martin GS, Eaton S, Mealer M, Moss M (2005) Extravascular lung water in patients with severe sepsis: a prospective cohort study. Crit Care 9:R74–R82

    Article  PubMed  Google Scholar 

  3. Isakow W, Schuster DP (2006) Extravascular lung water measurements and hemodynamic monitoring in the critically ill: bedside alternatives to the pulmonary artery catheter. Am J Physiol Lung Cell Mol Physiol 291:L1118–L1131

    Article  PubMed  CAS  Google Scholar 

  4. Tatsushi M, Kazumata K, Ajiki M, Ushikoshi S, Terasaka S (2007) Goal-directed fluid management by bedside transpulmonary hemodynamic monitoring after subarachnoid hemorrhage. Stroke 38:3218–3224

    Article  Google Scholar 

  5. Pistolesi M, Giuntini C (1978) Assessment of extravascular lung water. Radiol Clin North Am 15:551–574

    Google Scholar 

  6. Rouby JJ, Puybasset L, Cluzel P, Richecoeur J, Lu Q, Grenier P (2000) Regional distribution of gas and tissue in acute respiratory distress syndrome. II. Physiological correlations and definition of an ARDS severity score: CT scan ARDS study group. Intensive Care Med 26:1046–1056

    Article  PubMed  CAS  Google Scholar 

  7. Lichtenstein D, Goldstein I, Mourgeon E, Cluzel P, Grenier P, Rouby JJ (2004) Comparative diagnostic performances of auscultation, chest radiography, and lung ultrasonography in acute respiratory distress syndrome. Anesthesiology 100:9–15

    Article  PubMed  Google Scholar 

  8. Roch A, Michelet P, Lambert D, Delliaux S, Saby C, Perrin G, Ghez O, Bregeon F, Thomas P, Carpentier JP, Papazian L, Auffray JP (2004) Accuracy of the double indicator method for measurement of extravascular lung water depends on the type of acute lung injury. Crit Care Med 32:811–817

    Article  PubMed  Google Scholar 

  9. Sakka SG, Reinhart K, Meier-Hellmann A (1999) Comparison of pulmonary artery and arterial thermodilution cardiac output in critically ill patients. Intensive Care Med 25:843–846

    Article  PubMed  CAS  Google Scholar 

  10. Rossi P, Wanecek M, Rudehill A, Konrad D, Weitzberg E, Oldner A (2006) Comparison of a single indicator and gravimetric technique for estimation of extravascular lung water in endotoxemic pigs. Crit Care Med 34:1437

    Article  PubMed  Google Scholar 

  11. Picano E (2004) Sustainability of medical imaging. BMJ 328:578–580

    Article  PubMed  Google Scholar 

  12. Lichtenstein D, Meziere G, Biderman P, Gepner A, Barre O (1997) The comet-tail artifact. An ultrasound sign of alveolar-interstitial syndrome. Am J Respir Crit Care Med 156:1640–1646

    PubMed  CAS  Google Scholar 

  13. Picano E, Frassi F, Agricola E, Gligorova S, Gargani L, Mottola G (2006) Ultrasound lung comets: a clinically useful sign of extravascular lung water. J Am Soc Echocardiogr 19:356–363

    Article  PubMed  Google Scholar 

  14. Copetti R, Soldati G, Copetti P (2008) Chest sonography: a useful tool to differentiate acute cardiogenic pulmonary edema from acute respiratory distress syndrome. Cardiovasc Ultrasound 6:16

    Article  PubMed  Google Scholar 

  15. Volpicelli G, Elbarbary M, Blaivas M, Lichtenstein DA, Mathis G, Kirkpatrick AW, Melniker L, Gargani L, Noble VE, Via G, Dean A, Tsung JW, Soldati G, Copetti R, Bouhemad B, Reissig A, Agricola E, Rouby JJ, Arbelot C, Liteplo A, Sargsyan A, Silva F, Hoppmann R, Breitkreutz R, Seibel A, Neri L, Storti E, Petrovic T; International Liaison Committee on Lung Ultrasound (ILC-LUS) for International Consensus Conference on Lung Ultrasound (ICC-LUS) (2012) International evidence-based recommendations for point-of-care lung ultrasound. Intensive Care Med 38:577–591

    Article  PubMed  Google Scholar 

  16. Gargani L (2011) Lung ultrasound: a new tool for the cardiologist. Cardiovasc Ultrasound 9:6

    Article  PubMed  Google Scholar 

  17. Lichtenstein D, Meziere G, Biderman P, Gepner A (2000) The “lung point”: an ultrasound sign specific to pneumothorax. Intensive Care Med 26:1434–1440

    Article  PubMed  CAS  Google Scholar 

  18. Rowan KR, Kirkpatrick AW, Liu D, Forkheim KE, Mayo JR, Nicolaou S (2002) Traumatic pneumothorax detection with thoracic US: correlation with chest radiography and CT – initial experience. Radiology 225:210–214

    Article  PubMed  Google Scholar 

  19. Lichtenstein D, Pinsky MR, Jardin F (2007) General ultrasound in the critically ill. Springer, Berlin

    Google Scholar 

  20. Gryminski J, Krakowka P, Lypacewicz G (1976) The diagnosis of pleural effusion by ultrasonic and radiologic techniques. Chest 70:33

    Article  PubMed  CAS  Google Scholar 

  21. Yang P, Luh K, Chang D, Wu H, Yu C, Kuo S (1992) Value of sonography in determining the nature of pleural effusion: analysis of 320 cases. AJR Am J Roentgenol 159:29

    PubMed  CAS  Google Scholar 

  22. Mathis G, Blank W, Reissig A, Lechleitner P, Reuss J, Schuler A, Beckh S (2005) Thoracic ultrasound for diagnosing pulmonary embolism: a prospective multicenter study of 352 patients. Chest 128:1531–1538

    Article  PubMed  Google Scholar 

  23. Reissig AK (2007) Sonographic diagnosis and follow-up of pneumonia: a prospective study. Respiration 74:537–547

    Article  PubMed  Google Scholar 

  24. Gargani L, Frassi F, Soldati G, Tesorio P, Gheorghiade M, Picano E (2008) Ultrasound lung comets for the differential diagnosis of acute cardiogenic dyspnoea: a comparison with natriuretic peptides. Eur J Heart Fail 10:70–77

    Article  PubMed  CAS  Google Scholar 

  25. Soldati G, Copetti R, Sher S (2009) Sonographic interstitial syndrome: the sound of lung water. J Ultrasound Med 28:163–174

    PubMed  Google Scholar 

  26. Volpicelli G, Caramello V, Cardinale L, Mussa A, Bar F, Frascisco MF (2008) Bedside ultrasound of the lung for the monitoring of acute decompensated heart failure. Am J Emerg Med 26:585–591

    Article  PubMed  Google Scholar 

  27. Jambrik Z, Monti S, Coppola V, Agricola E, Mottola G, Miniati M, Picano E (2004) Usefulness of ultrasound lung comets as a nonradiologic sign of extravascular lung water. Am J Cardiol 15:1265–1270

    Article  Google Scholar 

  28. Agricola E, Bove T, Oppizzi M, Marino G, Zangrillo A, Margonato A, Picano E (2005) “Ultrasound comet-tail images”: a marker of pulmonary edema: a comparative study with wedge pressure and extravascular lung water. Chest 127:1690–1695

    Article  PubMed  Google Scholar 

  29. Gargani L, Lionetti V, Di Cristofano C, Bevilacqua G, Recchia FA, Picano E (2007) Early detection of acute lung injury uncoupled to hypoxemia in pigs using ultrasound lung comets. Crit Care Med 35:2769–2774

    Article  PubMed  Google Scholar 

  30. Lichtenstein D (2012) Fluid administration limited by lung sonography: the place of lung ultrasound in assessment of acute circulatory failure (the FALLS-protocol). Expert Rev Respir Med 6:155–162

    Article  PubMed  Google Scholar 

  31. Martin K, Ibáñez L, Avila L, Barré S, Kaspersen J (2005) Integrating segmentation methods from the insight toolkit into a visualization application. Med Image Anal 9:579–593

    Article  PubMed  Google Scholar 

  32. Drummond GB (1998) Computed tomography and pulmonary measurements. Br J Anaesth 80:665–671

    Article  PubMed  CAS  Google Scholar 

  33. Malbouisson LM, Preteux F, Puybasset L, Grenier P, Coriat P, Rouby JJ (2001) Validation of a software designed for computed tomographic (CT) measurement of lung water. Intensive Care Med 27:602–608

    Article  PubMed  CAS  Google Scholar 

  34. Pelosi P, Goldner M, McKibben A, Adams A, Eccher G, Caironi P, Losappio S, Gattinoni L, Marini JJ (2001) Recruitment and derecruitment during acute respiratory failure: an experimental study. Am J Respir Crit Care Med 164:122–130

    PubMed  CAS  Google Scholar 

  35. Patroniti N, Bellani G, Manfio A, Maggioni E, Giuffrida A, Foti G, Pesenti A (2004) Lung volume in mechanically ventilated patients: measurement by simplified helium dilution compared to quantitative CT scan. Intensive Care Med 30:282–289

    Article  PubMed  Google Scholar 

  36. Puybasset L, Cluzel P, Gusman P, Grenier P, Preteux F, Rouby JJ (2000) Regional distribution of gas and tissue in acute respiratory distress syndrome. I. Consequences for lung morphology. CT scan ARDS study group. Intensive Care Med 26:857–869

    Article  PubMed  CAS  Google Scholar 

  37. Gargani L, Doveri M, D’Errico L, Frassi F, Bazzichi ML, Delle Sedie A, Scali MC, Monti S, Mondillo S, Bombardieri S, Caramella D, Picano E (2009) Ultrasound lung comets in systemic sclerosis: a chest sonography hallmark of pulmonary interstitial fibrosis. Rheumatology (Oxford) 48:1382–1387

    Article  Google Scholar 

  38. Bouhemad B, Brisson H, Le-Guen M, Arbelot C, Lu Q, Rouby JJ (2011) Bedside ultrasound assessment of positive end-expiratory pressure-induced lung recruitment. Am J Respir Crit Care Med 183:341–347

    Article  PubMed  Google Scholar 

  39. Bouhemad B, Liu ZH, Arbelot C, Zhang M, Ferarri F, Le-Guen M, Girard M, Lu Q, Rouby JJ (2010) Ultrasound assessment of antibiotic-induced pulmonary reaeration in ventilator-associated pneumonia. Crit Care Med 38:84

    Article  PubMed  Google Scholar 

  40. Bellani G, Messa C, Guerra L, Spagnolli E, Foti G, Patroniti N, Fumagalli R, Musch G, Fazio F, Pesenti A (2009) Lungs of patients with acute respiratory distress syndrome show diffuse inflammation in normally aerated regions: a [18F]-fluoro-2-deoxy-d-glucose PET/CT study. Crit Care Med 37:2216

    Article  PubMed  Google Scholar 

  41. Mallamaci F, Benedetto F, Tripepi R, Rastelli S, Castellino P, Tripepi G, Picano E, Zoccali C (2010) Detection of pulmonary congestion by chest ultrasound in dialysis patients. JACC Cardiovasc Imaging 3:586–594

    Article  PubMed  Google Scholar 

  42. Bedetti G, Gargani L, Corbisiero A, Frassi F, Poggianti E, Mottola G (2006) Evaluation of ultrasound lung comets by hand-held echocardiography. Cardiovasc Ultrasound 4:34

    Article  PubMed  CAS  Google Scholar 

  43. Shah S, Noble VE, Umulisa I, Dushimiyimana JM, Bukhman G, Mukherjee J, Rich M, Epino H (2008) Development of an ultrasound training curriculum in a limited resource international setting: successes and challenges of ultrasound training in rural Rwanda. Int J Emerg Med 1(3):193–196

    Google Scholar 

  44. Jambrik Z, Gargani L, Adamicza A, Kaszaki J, Varga A, Forster T, Boros M, Picano E (2010) B-lines quantify the lung water content: a lung ultrasound versus lung gravimetry study in acute lung injury. Ultrasound Med Biol 36:2004–2010

    Article  PubMed  Google Scholar 

  45. Delle Sedie A, Doveri M, Frassi F, Gargani L, D’Errico G, Pepe P, Bazzichi L, Riente L, Caramella D, Bombardieri S (2010) Ultrasound lung comets in systemic sclerosis: a useful tool to detect lung interstitial fibrosis. Clin Exp Rheumatol 28(5 Suppl 62):S54

    PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Surgery, University of Pisa, Via Paradisa, 2, 56100, Pisa, Italy

    Giacomo Baldi, Antonio Abramo, Francesco Giunta & Francesco Forfori

  2. Institute of Clinical Physiology, National Research Council, Pisa, Italy

    Luna Gargani & Eugenio Picano

  3. Department of Oncology, Transplants and New Medical Technologies, University of Pisa, Pisa, Italy

    Luigia D’Errico & Davide Caramella

Authors
  1. Giacomo Baldi
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Luna Gargani
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Antonio Abramo
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Luigia D’Errico
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Davide Caramella
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Eugenio Picano
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Francesco Giunta
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Francesco Forfori
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Giacomo Baldi.

Addendum: data analysis

Addendum: data analysis

Lung weight and volume

These variables are derived by summing voxel volumes (V v) and attenuation values (A v) over the voxels pertaining to the lungs. Therefore, the difference in the calculated lung weight and density between a fine-grained and a coarse-grained CT scan is due to the coarser approximation of the contour lung regions only. The average voxel volume was 1.21 ± 0.31 mm3.

To calculate the weight of a voxel we applied a previously described method [33–35]. The correctness of the calculation is based on some working assumptions: (1) there is a linear correlation between the A v in Hounsfield units and the physical density of the tissue it represents, (2) the physical density of non-aerated lung parenchyma (ρ) (including vessels and extravascular water) can be approximated as ρ = 1 kg/l, namely the same physical density of water, corresponding to 0 HU, (3) the physical density of air corresponds to attenuation values ≤−1,000 HU. This means a voxel with such an attenuation value contains only air.

For each lung voxel we calculated its weight in grams (W v) and its gas content in milliliters (G v) by considering three cases:

  • A v ≥ 0: V v is entirely represented by lung parenchyma and therefore W v = ρ × V v, whereas G v = 0

  • Av ≤ −1,000: V v is entirely represented by gas and therefore W v = 0, whereas G v = V v

  • −1,000 < A v < 0: V v is partially represented by gas and the rest by parenchyma. According to assumption 1, we can write G v = A v × V v/−1,000. Consequently, W v = (V v − G v) × ρ.

Segmentation algorithm

The first step in our algorithm was to segment the structures that are easily identifiable (e.g., trachea, main bronchi, bones) and to remove them from the image. In a second step, we selected a voxel as the starting point for the segmented region. Every voxel surrounding the segmented region was then automatically tested against an attenuation value threshold (0 HU or less) and included in the segmented region, if the test was passed. The process was repeated until no new voxels could be added to the segmented region. As a last step, we filtered the image in the segmented region to include smaller areas (e.g., consolidations, vessels, etc.) with attenuation values greater than 0 HU that did not pass the threshold test, but that were clearly inside the lung.

This computer-assisted methodology allowed us to perform lung segmentation in a very short time. Normal lungs could be segmented in less than 5 min which is much less time than is needed for manual segmentation. When lungs were very edematous, with pleural effusions and atelectasis, the time required increased and part of the segmentation task had to be performed manually.

Ultrasound beam simulation

Ultrasound energy is rapidly dissipated as the beam encounters alveolar air. Therefore, LUS actually provides information on a peripheral layer of subpleural lung tissue. The thickness of the LUS-explorable layer increases when the air content of the lung diminishes because the ultrasound beam is less reflected. We performed a mathematical simulation to estimate the distance traveled by the ultrasound beam as it is dissipated by the subpleural SLD extrapolated from CT images.

First, we segmented the CT scans considering only the anterolateral portions of the thorax corresponding to the lung surface evaluated by LUS. This was achieved by manually cutting the images at the mid-axillary lines. We then constructed a series of ten 1-mm thick concentric subpleural layers for each lung, and calculated their volume, weight and density (Fig. 1). Subpleural layers were automatically constructed by passing the segmented images through a distance filter that selects only voxels that are at most x millimeters away from the external contour of the lung, repeating the process for x in the range 1–10. We decided to construct ten layers corresponding to 1 cm of subpleural lung tissue because we estimated it to be the maximum distance reachable by the ultrasound beam. The upper and lower lobes were segmented using the same algorithm employed for construction of subpleural layers, simply increasing the distance filter tolerance.

We then used the resulting data to simulate the energy loss of the ultrasound beam as it travels through consecutive layers of tissue. The simulation is simply performed by calculating the energy reflection coefficient (R) between two interfaces with different acoustic impedance. More precisely R = [(Z 2−Z 1)/(Z 2 + Z 1)]2 where Z 1 and Z 2 are the acoustic impedances of two adjacent subpleural layers, respectively. We considered the point where 99 % of the beam energy is dissipated as the maximum distance (d M) reached by the beam. We use d M to calculate the density of the subpleural layer (SLD) with the corresponding thickness.

Our simulation of beam energy loss was based on the assumption that air and tissue are uniformly distributed in each layer. Since this is not the case in real lung, we obtained an overestimation of the maximum distance reached. However, the difference between SLD and total lung density was generally very small after the second subpleural layer (SLD − lung density = 0.03 ± 0.05 g/ml).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Baldi, G., Gargani, L., Abramo, A. et al. Lung water assessment by lung ultrasonography in intensive care: a pilot study. Intensive Care Med 39, 74–84 (2013). https://doi.org/10.1007/s00134-012-2694-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00134-012-2694-x

Keywords

Profiles

  1. Francesco Forfori View author profile