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

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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.

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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 [3335]. 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 2Z 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).

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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

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