Electrical Impedance Tomography Identifies Evolution of Regional Perfusion in a Porcine Model of Acute Respiratory Distress Syndrome.

BACKGROUND: Bedside electrical impedance tomography could be useful to visualize evolving pulmonary perfusion distributions when acute respiratory distress syndrome worsens or in response to ventilatory and positional therapies. In experimental acute respiratory distress syndrome, this study evaluated the agreement of electrical impedance tomography and dynamic contrast-enhanced computed tomography perfusion distributions at two injury time points and in response to increased positive end-expiratory pressure (PEEP) and prone position. METHODS: Eleven mechanically ventilated (VT 8 ml · kg-1) Yorkshire pigs (five male, six female) received bronchial hydrochloric acid (3.5 ml · kg-1) to invoke lung injury. Electrical impedance tomography and computed tomography perfusion images were obtained at 2 h (early injury) and 24 h (late injury) after injury in supine position with PEEP 5 and 10 cm H2O. In eight animals, electrical impedance tomography and computed tomography perfusion imaging were also conducted in the prone position. Electrical impedance tomography perfusion (QEIT) and computed tomography perfusion (QCT) values (as percentages of image total) were compared in eight vertical regions across injury stages, levels of PEEP, and body positions using mixed-effects linear regression. The primary outcome was agreement between QEIT and QCT, defined using limits of agreement and Pearson correlation coefficient. RESULTS: Pao2/Fio2 decreased over the course of the experiment (healthy to early injury, -253 [95% CI, -317 to -189]; early to late injury, -88 [95% CI, -151 to -24]). The limits of agreement between QEIT and QCT were -4.66% and 4.73% for the middle 50% quantile of average regional perfusion, and the correlation coefficient was 0.88 (95% CI, 0.86 to 0.90]; P < 0.001). Electrical impedance tomography and computed tomography showed similar perfusion redistributions over injury stages and in response to increased PEEP. QEIT redistributions after positional therapy underestimated QCT in ventral regions and overestimated QCT in dorsal regions. CONCLUSIONS: Electrical impedance tomography closely approximated computed tomography perfusion measures in experimental acute respiratory distress syndrome, in the supine position, over injury progression and with increased PEEP. Further validation is needed to determine the accuracy of electrical impedance tomography in measuring perfusion redistributions after positional changes.

Improving pulmonary perfusion assessment by dynamic contrast-enhanced computed tomography in an experimental lung injury model.

Pulmonary perfusion has been poorly characterized in acute respiratory distress syndrome (ARDS). Optimizing protocols to measure pulmonary blood flow (PBF) via dynamic contrast-enhanced (DCE) computed tomography (CT) could improve understanding of how ARDS alters pulmonary perfusion. In this study, comparative evaluations of injection protocols and tracer-kinetic analysis models were performed based on DCE-CT data measured in ventilated pigs with and without lung injury. Ten Yorkshire pigs (five with lung injury, five healthy) were anesthetized, intubated, and mechanically ventilated; lung injury was induced by bronchial hydrochloric acid instillation. Each DCE-CT scan was obtained during a 30-s end-expiratory breath-hold. Reproducibility of PBF measurements was evaluated in three pigs. In eight pigs, undiluted and diluted Isovue-370 were separately injected to evaluate the effect of contrast viscosity on estimated PBF values. PBF was estimated with the peak-enhancement and the steepest-slope approach. Total-lung PBF was estimated in two healthy pigs to compare with cardiac output measured invasively by thermodilution in the pulmonary artery. Repeated measurements in the same animals yielded a good reproducibility of computed PBF maps. Injecting diluted isovue-370 resulted in smaller contrast-time curves in the pulmonary artery (P < 0.01) and vein (P < 0.01) without substantially diminishing peak signal intensity (P = 0.46 in the pulmonary artery) compared with the pure contrast agent since its viscosity is closer to that of blood. As compared with the peak-enhancement model, PBF values estimated by the steepest-slope model with diluted contrast were much closer to the cardiac output (R2 = 0.82) as compared with the peak-enhancement model. DCE-CT using the steepest-slope model and diluted contrast agent provided reliable quantitative estimates of PBF.NEW & NOTEWORTHY Dynamic contrast-enhanced CT using a lower-viscosity contrast agent in combination with tracer-kinetic analysis by the steepest-slope model improves pulmonary blood flow measurements and assessment of regional distributions of lung perfusion.

Unsupervised segmentation and quantification of COVID-19 lesions on computed Tomography scans using CycleGAN.

BACKGROUND: Lesion segmentation is a critical step in medical image analysis, and methods to identify pathology without time-intensive manual labeling of data are of utmost importance during a pandemic and in resource-constrained healthcare settings. Here, we describe a method for fully automated segmentation and quantification of pathological COVID-19 lung tissue on chest Computed Tomography (CT) scans without the need for manually segmented training data. METHODS: We trained a cycle-consistent generative adversarial network (CycleGAN) to convert images of COVID-19 scans into their generated healthy equivalents. Subtraction of the generated healthy images from their corresponding original CT scans yielded maps of pathological tissue, without background lung parenchyma, fissures, airways, or vessels. We then used these maps to construct three-dimensional lesion segmentations. Using a validation dataset, Dice scores were computed for our lesion segmentations and other published segmentation networks using ground truth segmentations reviewed by radiologists. RESULTS: The COVID-to-Healthy generator eliminated high Hounsfield unit (HU) voxels within pulmonary lesions and replaced them with lower HU voxels. The generator did not distort normal anatomy such as vessels, airways, or fissures. The generated healthy images had higher gas content (2.45 ± 0.93 vs 3.01 ± 0.84 L, P < 0.001) and lower tissue density (1.27 ± 0.40 vs 0.73 ± 0.29 Kg, P < 0.001) than their corresponding original COVID-19 images, and they were not significantly different from those of the healthy images (P < 0.001). Using the validation dataset, lesion segmentations scored an average Dice score of 55.9, comparable to other weakly supervised networks that do require manual segmentations. CONCLUSION: Our CycleGAN model successfully segmented pulmonary lesions in mild and severe COVID-19 cases. Our model's performance was comparable to other published models; however, our model is unique in its ability to segment lesions without the need for manual segmentations.

CT image segmentation for inflamed and fibrotic lungs using a multi-resolution convolutional neural network.

The purpose of this study was to develop a fully-automated segmentation algorithm, robust to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of computed tomography images. A polymorphic training approach is proposed, in which both specifically labeled left and right lungs of humans with COPD, and nonspecifically labeled lungs of animals with acute lung injury, were incorporated into training a single neural network. The resulting network is intended for predicting left and right lung regions in humans with or without diffuse opacification and consolidation. Performance of the proposed lung segmentation algorithm was extensively evaluated on CT scans of subjects with COPD, confirmed COVID-19, lung cancer, and IPF, despite no labeled training data of the latter three diseases. Lobar segmentations were obtained using the left and right lung segmentation as input to the LobeNet algorithm. Regional lobar analysis was performed using hierarchical clustering to identify radiographic subtypes of COVID-19. The proposed lung segmentation algorithm was quantitatively evaluated using semi-automated and manually-corrected segmentations in 87 COVID-19 CT images, achieving an average symmetric surface distance of [Formula: see text] mm and Dice coefficient of [Formula: see text]. Hierarchical clustering identified four radiographical phenotypes of COVID-19 based on lobar fractions of consolidated and poorly aerated tissue. Lower left and lower right lobes were consistently more afflicted with poor aeration and consolidation. However, the most severe cases demonstrated involvement of all lobes. The polymorphic training approach was able to accurately segment COVID-19 cases with diffuse consolidation without requiring COVID-19 cases for training.

CT Image Segmentation for Inflamed and Fibrotic Lungs Using a Multi-Resolution Convolutional Neural Network.

The purpose of this study was to develop a fully-automated segmentation algorithm, robust to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of computed tomography images. A polymorphic training approach is proposed, in which both specifically labeled left and right lungs of humans with COPD, and nonspecifically labeled lungs of animals with acute lung injury, were incorporated into training a single neural network. The resulting network is intended for predicting left and right lung regions in humans with or without diffuse opacification and consolidation. Performance of the proposed lung segmentation algorithm was extensively evaluated on CT scans of subjects with COPD, confirmed COVID-19, lung cancer, and IPF, despite no labeled training data of the latter three diseases. Lobar segmentations were obtained using the left and right lung segmentation as input to the LobeNet algorithm. Regional lobar analysis was performed using hierarchical clustering to identify radiographic subtypes of COVID-19. The proposed lung segmentation algorithm was quantitatively evaluated using semi-automated and manually-corrected segmentations in 87 COVID-19 CT images, achieving an average symmetric surface distance of $0.495 \pm 0.309$ mm and Dice coefficient of $0.985 \pm 0.011$. Hierarchical clustering identified four radiographical phenotypes of COVID-19 based on lobar fractions of consolidated and poorly aerated tissue. Lower left and lower right lobes were consistently more afflicted with poor aeration and consolidation. However, the most severe cases demonstrated involvement of all lobes. The polymorphic training approach was able to accurately segment COVID-19 cases with diffuse consolidation without requiring COVID-19 cases for training.

Positional Therapy and Regional Pulmonary Ventilation.

BACKGROUND: Prone ventilation redistributes lung inflation along the gravitational axis; however, localized, nongravitational effects of body position are less well characterized. The authors hypothesize that positional inflation improvements follow both gravitational and nongravitational distributions. This study is a nonoverlapping reanalysis of previously published large animal data. METHODS: Five intubated, mechanically ventilated pigs were imaged before and after lung injury by tracheal injection of hydrochloric acid (2 ml/kg). Computed tomography scans were performed at 5 and 10 cm H2O positive end-expiratory pressure (PEEP) in both prone and supine positions. All paired prone-supine images were digitally aligned to each other. Each unit of lung tissue was assigned to three clusters (K-means) according to positional changes of its density and dimensions. The regional cluster distribution was analyzed. Units of tissue displaying lung recruitment were mapped. RESULTS: We characterized three tissue clusters on computed tomography: deflation (increased tissue density and contraction), limited response (stable density and volume), and reinflation (decreased density and expansion). The respective clusters occupied (mean ± SD including all studied conditions) 29.3 ± 12.9%, 47.6 ± 11.4%, and 23.1 ± 8.3% of total lung mass, with similar distributions before and after lung injury. Reinflation was slightly greater at higher PEEP after injury. Larger proportions of the reinflation cluster were contained in the dorsal versus ventral (86.4 ± 8.5% vs. 13.6 ± 8.5%, P < 0.001) and in the caudal versus cranial (63.4 ± 11.2% vs. 36.6 ± 11.2%, P < 0.001) regions of the lung. After injury, prone positioning recruited 64.5 ± 36.7 g of tissue (11.4 ± 6.7% of total lung mass) at lower PEEP, and 49.9 ± 12.9 g (8.9 ± 2.8% of total mass) at higher PEEP; more than 59.0% of this recruitment was caudal. CONCLUSIONS: During mechanical ventilation, lung reinflation and recruitment by the prone positioning were primarily localized in the dorso-caudal lung. The local effects of positioning in this lung region may determine its clinical efficacy.