Imaging the acute respiratory distress syndrome: past, present and future

Abstract

In patients with the acute respiratory distress syndrome (ARDS), lung imaging is a fundamental tool in the study of the morphological and mechanistic features of the lungs. Chest computed tomography studies led to major advances in the understanding of ARDS physiology. They allowed the in vivo study of the syndrome's lung features in relation with its impact on respiratory physiology and physiology, but also explored the lungs' response to mechanical ventilation, be it alveolar recruitment or ventilator-induced lung injuries. Coupled with positron emission tomography, morphological findings were put in relation with ventilation, perfusion or acute lung inflammation. Lung imaging has always been central in the care of patients with ARDS, with modern point-of-care tools such as electrical impedance tomography or lung ultrasounds guiding clinical reasoning beyond macro-respiratory mechanics. Finally, artificial intelligence and machine learning now assist imaging post-processing software, which allows real-time analysis of quantitative parameters that describe the syndrome's complexity. This narrative review aims to draw a didactic and comprehensive picture of how modern imaging techniques improved our understanding of the syndrome, and have the potential to help the clinician guide ventilatory treatment and refine patient prognostication.

Keywords: Acute respiratory distress syndrome; Computed tomography; Electrical impedance tomography; Lung ultrasounds; Positron emission tomography; Ventilator-induced lung injuries.

Fig. 1

Quantitative CT analysis. The figure shows how the CT number of 9 schematic voxels may inform the changes of aeration that the lungs undergo during ARDS and in response to ventilatory management. Panel 1 shows 9 normally inflated voxels (blue, CT number between − 900 and − 501 HU). Once ARDS occurs (panel 2), a number of voxels become poorly inflated (n = 2, yellow, CT between − 500 and − 101 HU) and non-inflated (n = 3, green, CT number between − 100 and 100 HU) despite the application of 5 cmH₂O of PEEP. When PEEP is increased to 10 cmH₂O (panel 3), a poorly inflated voxel is restored to normal inflation, but a normally inflated voxel becomes hyperinflated (n = 1, red, CT number below − 901 HU). During tidal ventilation (panel 4), voxels pass from normal inflation to hyperinflation (tidal hyperinflation), while others are passing from non-inflation to normal inflation (tidal opening and closing, or atelectrauma). ARDS acute respiratory distress syndrome, CT computed tomography, HU Hounsfield unit, PEEP positive end-expiratory pressure

Fig. 2

Representative lung CTs before and after ARDS induction in swine. The upper part of the figure shows 2 CT slice at mid-chest level in the same animal under mechanical ventilation, general anesthesia and neuromuscular blockade. CTs are acquired at end-expiration, before and after experimental ARDS is induced by intratracheal instillation of hydrochloric acid. The CT image on the right shows typical ARDS CT findings, with dependent alveolar consolidation and ground-glass opacities in the mid-level section. The medial part of the figure shows the distribution of CT numbers of the CT slices presented above, after the exclusion of extra-pulmonary areas by manual segmentation. To better represent the change in inflation, CT numbers were classified based on 4 inflation compartments: non-inflated (CT number between − 100 and 100 HU), poorly inflated (CT number between − 500 and − 101 HU), normally inflated (CT number between − 900 and − 501 HU) and hyperinflated (CT number below − 900 HU). The histograms clearly demonstrated how the non-aerated compartment increases, and the normally inflated compartment is diminished as a consequence of experimental ARDS. Finally, the lower part of the figure shows the parametric images of inhomogeneities based on the patented method by Cressoni and colleagues in the same CT slices (patent WO 2013/088336A1 “Method for determining inhomogeneity in animal tissue and equipment to implement it”). The image on the right shows how ARDS increases lung inhomogeneities, especially in zones at the interface of regions with different aeration (voxels in red). ARDS acute respiratory distress syndrome, CT computed tomography, HU Hounsfield unit

Fig. 3

Inflation response to PEEP evaluated in lung EIT and quantitative CT in patients with ARDS. The figure shows the average proportion of the 3 inflation compartments derived from the innovative methods proposed by Beda and colleagues in which EIT ∆Z, as a surrogate of volume, is used to compute a pressure-volume response curve at each PEEP level of a decremental PEEP ramp. Red bars are the proportion of voxels estimated to be exposed to tidal hyperinflation, blue bars those that undergo tidal collapse, and green bars voxels in which the change in the P-V curve is linear and ventilation is considered homogenous. These EIT-derived parameters are compared to CT-derived inflation compartments at end-expiration and at the same PEEP levels (on the right side). Inflation compartments in CT are defined as follows: non-aerated (− 100 to 100 HU, black bars), poorly aerated (-500 to -101 HU, dark grey bars), normally aerated (− 900 to 501 HU, light grey bars), and hyperaerated (− 1000 to − 901 HU, white bars). The authors conclude that there was a significant correlation between the EIT-computed overdistension parameter and non-inflated volumes at low PEEP levels, and with hyperinflated volumes at high PEEP levels, although the strength of correlation were weak. CT computed tomography, EIT electrical impedance tomography, PEEP positive end-expiratory pressure. Reproduced with permission from Wolters Kluwer Health [42]

Fig. 4

Prone position-induced changes, in interaction with PEEP, in ventilation and perfusion along the dorso-ventral gradient in an experimental model of ARDS. The figure shows the mean normalized values of regional ventilation (A) and perfusion (B), measured in PET by inhaled ¹³ N and injected [¹⁵O]-H₂O, in 10 lung sections distributed along the dorso-ventral gradient of animals in the supine (SP) or the prone position (PP) and with zero end-expiratory pressure (ZEEP) or a PEEP of 10 cmH₂O (PEEP) The figure shows that perfusion is redistributed to ventral lung regions, to a greater degree compared to regional ventilation, when a prone position is performed in conjunction with the application of PEEP. PEEP positive end-expiratory pressure, PET positron emission tomography, PP prone position, SP supine position, ZEEP zero end-expiratory pressure. Reproduced with permission from Wolters Kluwer Health [50]

Fig. 5

Lung cellular metabolic activity assessed in PET in animals with ARDS in the supine and prone position. The figure shows the tissue-corrected [¹⁸F]-fluorodeoxyglucose cellular uptake rate, a marker of cellular metabolism and inflammation in the lungs of sheep before and after 24 h of experimental ARDS and mechanical ventilation. Animals are compared based on their body position (supine vs. prone). The figure illustrates how prone positioning (PP) may decrease the inflammatory response of dependent regions that appear in supine animals after 24 h. The article from which this figure is taken suggests that the prevention of lung inflammation is related to the decrease in regional lung strain allowed by PP in posterior regions. Adapted with permission from the American Thoracic Society. Copyright © 2022 American Thoracic Society. All rights reserved [39]

Fig. 6

Acute lung macrophage inflammation in response to high tidal volume ventilation. The figure shows the coupled PET-CT acquisitions in 3 lung slices acquired in the same animal, before and after 4 h of high tidal ventilation (targeting a transpulmonary pressure between 35 and 40 cmH₂O). PET was performed using [11C](R)-PK11195, a TSPO-specific PET radiotracer that allow the non-invasive quantification of lung macrophages. CT acquisitions are performed at end-inspiration and are shown as parametric images in which the voxel’s CT number is expressed based on the 4 inflation compartments (see color scale). The figure shows how the radiotracer’s lung uptake (in SUV) is increased in all lung regions after injurious ventilation; this is especially true in ventral lung areas, where hyperinflation is distributed (red voxels on the end-inspiratory CT slices). CT computed tomography, HU Hounsfield unit, PET positron emission tomography, SUV standardized uptake value

Fig. 7

Lung CT histograms of patients with COVID-19 and non-COVID-19-associated ARDS. The figure shows the distribution of lung CT numbers (normalized to whole lung volume) quantified on the whole lung at end-expiration in 10 patients with non-COVID-19-associated ARDS (panel A), 5 patients with COVID-19-associated ARDS and high respiratory system elastance (panel B, type H, Elastance > 20 cmH₂O.L⁻¹), and 8 patients with COVID-19-associated ARDS and low respiratory system elastance (panel C, type L, Elastance ≤ 20 cmH₂O.L⁻¹). Red bars represent intervals within the hyperinflation range, blue bars intervals within the normal inflation range, yellow intervals within the poor inflation range, and green intervals within the non-aeration range. The figure shows how COVID-19 ARDS type H patients demonstrate a loss in lung aeration in lung CT analysis similar to that of non-COVID-19 patients, with a shift towards 0 of CT numbers, while type L patients show a relatively normal CT numbers distribution, with a high prevalence of normally inflated voxels (in blue). Panel D summarizes the findings of the 3 other panels using fitted values to observed data. ARDS acute respiratory distress syndrome, CT computed tomography. Reproduced with permission from Elsevier [65]. Copyright (2020)

Fig. 8

Future of ARDS imaging. The figure shows the development steps of ARDS-specific imaging, which will rely on the conjunct use of point-of-care cameras in association with fast-response software. The information extracted for the imaging acquisitions at the bedside will then inform the clinician on the ARDS lung degree of illness and response to ventilation, as well as rapidly detect complications, in relation to the integration of these elements with other ICU monitoring devices and information. These elements may then help guide therapy, by the modification of ventilator settings or the administration of non-ventilatory strategies, based on the patient’s characteristics. This individualization of care allowed by ARDS imaging may then help improve patient outcome, given this is demonstrated in randomized controlled trials when the technology is ready. AI artificial intelligence, ARDS acute respiratory distress syndrome, ICU intensive care unit