Case Studies in Physiology: Temporal variations of the lung parenchyma and vasculature in asymptomatic COVID-19 pneumonia: a multispectral CT assessment.

This study reports systematic longitudinal pathophysiology of lung parenchymal and vascular effects of asymptomatic COVID-19 pneumonia in a young, healthy never-smoking male. Inspiratory and expiratory noncontrast along with contrast dual-energy computed tomography (DECT) scans of the chest were performed at baseline on the day of acute COVID-19 diagnosis (day 0), and across a 90-day period. Despite normal vital signs and pulmonary function tests on the day of diagnosis, the CT scans and corresponding quantification metrics detected abnormalities in parenchymal expansion based on image registration, ground-glass (GGO) texture (inflammation) as well as DECT-derived pulmonary blood volume (PBV). Follow-up scans on day 30 showed improvement in the lung parenchymal mechanics as well as reduced GGO and improved PBV distribution. Improvements in lung PBV continued until day 90. However, the heterogeneity of parenchymal mechanics and texture-derived GGO increased on days 60 and 90. We highlight that even asymptomatic COVID-19 infection with unremarkable vital signs and pulmonary function tests can have measurable effects on lung parenchymal mechanics and vascular pathophysiology, which may follow apparently different clinical courses. For this asymptomatic subject, post COVID-19 regional mechanics demonstrated persistent increased heterogeneity concomitant with return of elevated GGOs, despite early improvements in vascular derangement.NEW & NOTEWORTHY We characterized the temporal changes of lung parenchyma and microvascular pathophysiology from COVID-19 infection in an asymptomatic young, healthy nonsmoking male using dual-energy CT. Lung parenchymal mechanics and microvascular disease followed different clinical courses. Heterogeneous perfused blood volume became more uniform on follow-up visits up to 90 days. However, post COVID-19 mechanical heterogeneity of the lung parenchyma increased after apparent improvements in vascular abnormalities, even with normal spirometric indices.

Oscillatory ventilation redux: alternative perspectives on ventilator-induced lung injury in the acute respiratory distress syndrome.

For patients with the acute respiratory distress syndrome (ARDS), ventilation strategies that limit end-expiratory derecruitment and end-inspiratory overdistension are the only interventions to have significantly reduced the morbidity and mortality. For this reason, the use of high-frequency oscillatory ventilation (HFOV) was considered to be an ideal protective strategy, given its reliance on very low tidal volumes cycled at very high rates. However, results from clinical trials in adults with ARDS have demonstrated that HFOV does not improve clinical outcomes. Recent experimental and computational studies have shown that oscillation of a mechanically heterogeneous lung with multiple simultaneous frequencies can reduce parenchymal strain, improve gas exchange, and maintain lung recruitment at lower distending pressures compared to traditional 'single-frequency' HFOV. This review will discuss the theoretical rationale for the use of multiple oscillatory frequencies in ARDS, as well as the mechanisms by which it may reduce the risk for ventilator-induced lung injury.

Quantitative CT imaging and advanced visualization methods: potential application in novel coronavirus disease 2019 (COVID-19) pneumonia.

Increasingly, quantitative lung computed tomography (qCT)-derived metrics are providing novel insights into chronic inflammatory lung diseases, including chronic obstructive pulmonary disease, asthma, interstitial lung disease, and more. Metrics related to parenchymal, airway, and vascular anatomy together with various measures associated with lung function including regional parenchymal mechanics, air trapping associated with functional small airways disease, and dual-energy derived measures of perfused blood volume are offering the ability to characterize disease phenotypes associated with the chronic inflammatory pulmonary diseases. With the emergence of COVID-19, together with its widely varying degrees of severity, its rapid progression in some cases, and the potential for lengthy post-COVID-19 morbidity, there is a new role in applying well-established qCT-based metrics. Based on the utility of qCT tools in other lung diseases, previously validated supervised classical machine learning methods, and emerging unsupervised machine learning and deep-learning approaches, we are now able to provide desperately needed insight into the acute and the chronic phases of this inflammatory lung disease. The potential areas in which qCT imaging can be beneficial include improved accuracy of diagnosis, identification of clinically distinct phenotypes, improvement of disease prognosis, stratification of care, and early objective evaluation of intervention response. There is also a potential role for qCT in evaluating an increasing population of post-COVID-19 lung parenchymal changes such as fibrosis. In this work, we discuss the basis of various lung qCT methods, using case-examples to highlight their potential application as a tool for the exploration and characterization of COVID-19, and offer scanning protocols to serve as templates for imaging the lung such that these established qCT analyses have the best chance at yielding the much needed new insights.

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.

Shared Ventilation in the Era of COVID-19: A Theoretical Consideration of the Dangers and Potential Solutions.

BACKGROUND: The use of shared ventilation, or the simultaneous support of multiple patients connected in parallel to a single mechanical ventilator, is receiving considerable interest for addressing the severe shortage of mechanical ventilators available during the novel coronavirus pandemic (COVID-19). In this paper we highlight the potentially disastrous consequences of naïve shared ventilation, in which patients are simply connected in parallel to a ventilator without any regard to their individual ventilatory requirements. We then examine possible approaches for individualization of mechanical ventilation, using modifications to the breathing circuit that may enable tuning of individual tidal volumes and driving pressures during either volume-controlled ventilation (VCV) or pressure-controlled ventilation (PCV). METHODS: Breathing circuit modifications included a PEEP valve on each expiratory limb for both VCV and PCV, an adjustable constriction and one-way valve on the inspiratory limb for VCV, and a pressure-relief valve for peak inspiratory pressure reduction on the inspiratory limb for PCV. The ability to regulate individual tidal volumes using these breathing circuit modifications was tested both theoretically in computer simulations as well as experimentally in mechanical test lungs. RESULTS: In both the simulations and experimental measurements, naïve shared ventilation resulted in large imbalances across individual tidal volume delivery, dependent on imbalances across patient mechanical properties. The proposed breathing circuit modifications for shared VCV and shared PCV enabled optimization of tidal volume distributions. Individual tidal volume for one patient during shared VCV was sensitive to changes in the mechanical properties of other patients. By contrast, shared PCV enabled independent control of individual patient-received ventilation. CONCLUSIONS: Of the shared ventilation strategies considered, shared PCV, with the inclusion of in-line pressure-relief valves in the individual inspiratory and expiratory limbs, offers the greatest degree of safety and lowest risk of catastrophic mechanical interactions between multiple patients connected to a single ventilator.