Utilizing Forced Vital Capacity to Predict Low Lung Compliance and Select Intraoperative Tidal Volume During Thoracic Surgery

Interactive Segmentation of Lung Tissue and Lung Excursion in Thoracic Dynamic MRI Based on Shape-guided Convolutional Neural Networks

Purpose

Lung tissue and lung excursion segmentation in thoracic dynamic magnetic resonance imaging (dMRI) is a critical step for quantitative analysis of thoracic structure and function in patients with respiratory disorders such as Thoracic Insufficiency Syndrome (TIS). However, the complex variability of intensity and shape of anatomical structures and the low contrast between the lung and surrounding tissue in MR images seriously hamper the accuracy and robustness of automatic segmentation methods. In this paper, we develop an interactive deep-learning based segmentation system to solve this problem.

Material & Methods

Considering the significant difference in lung morphological characteristics between normal subjects and TIS subjects, we utilized two independent data sets of normal subjects and TIS subjects to train and test our model. 202 dMRI scans from 101 normal pediatric subjects and 92 dMRI scans from 46 TIS pediatric subjects were acquired for this study and were randomly divided into training, validation, and test sets by an approximate ratio of 5:1:4. First, we designed an interactive region of interest (ROI) strategy to detect the lung ROI in dMRI for accelerating the training speed and reducing the negative influence of tissue located far away from the lung on lung segmentation. Second, we utilized a modified 2D U-Net to segment the lung tissue in lung ROIs, in which the adjacent slices are utilized as the input data to take advantage of the spatial information of the lungs. Third, we extracted the lung shell from the lung segmentation results as the shape feature and inputted the lung ROIs with shape feature into another modified 2D U-Net to segment the lung excursion in dMRI. To evaluate the performance of our approach, we computed the Dice coefficient (DC) and max-mean Hausdorff distance (MM-HD) between manual and automatic segmentations. In addition, we utilized Coefficient of Variation (CV) to assess the variability of our method on repeated dMRI scans and the differences of lung tidal volumes computed from the manual and automatic segmentation results.

Results

The proposed system yielded mean Dice coefficients of 0.96±0.02 and 0.89±0.05 for lung segmentation in dMRI of normal subjects and TIS subjects, respectively, demonstrating excellent agreement with manual delineation results. The Coefficient of Variation and p-values show that the estimated lung tidal volumes of our approach are statistically indistinguishable from those derived by manual segmentations.

Conclusions

The proposed approach can be applied to lung tissue and lung excursion segmentation from dynamic MR images with high accuracy and efficiency. The proposed approach has the potential to be utilized in the assessment of patients with TIS via dMRI routinely.

Forced Vital Capacity and Low Frequency Reactance Area Measurements Are Associated with Asthma Control and Exacerbations

Introduction

Forced vital capacity (FVC) is often preserved in severe asthma unless there is evidence of either airway remodelling or air trapping. Area under the reactance curve (AX) can be used to assess small airways dysfunction related lung stiffness and is related to disease control in severe asthma.

Methods

We explore if there may be a potential synergistic interaction between FVC and AX in terms of impaired asthma control as ACQ and exacerbations requiring oral corticosteroids (OCS). We pragmatically defined < 100% and ≥ 1.0 kPa/L/s as impaired FVC or AX, respectively.

Results

Patients with combined impairment of FVC and AX had significantly worse asthma control as higher ACQ, more severe exacerbations requiring OCS and worse spirometry (FEV₁ and FEF_25–75) than those with impaired FVC but preserved AX.

Conclusion

This in turn supports using both spirometry and oscillometry to characterise airway physiology more comprehensively in patients with more severe asthma.

Driving pressure guided ventilation

Protective ventilation is a prevailing ventilatory strategy these days and is comprised of small tidal volume, limited inspiratory pressure, and application of positive end-expiratory pressure (PEEP). However, several retrospective studies recently suggested that tidal volume, inspiratory pressure, and PEEP are not related to patient outcomes, or only related when they influence the driving pressure. Therefore, this review introduces the concept of driving pressure and looks into the possibility of driving pressure-guided ventilation as a new ventilatory strategy, especially in thoracic surgery where postoperative pulmonary complications are common, and thus, lung protection is of utmost importance.