Surface Projection of CT Reconstructed Lung Volume for Regional Lung Mechanics Estimation: A Preliminary Analysis

Abstract

Estimating regional lung volume along bedside is critical for creating a fine level of digital twins model for optimized setting of mechanical ventilation. Non-contact techniques with structured light is capable of reconstructing chest motion to incorporate with ventilator recorded volume to assess the regional lung volume changes between left and right lung, while its extension to a higher resolution of estimation requires a relation map between the surface motion and inside lung capacity distribution. To establish the relation map, a 2D projection of 3D lung CT lung volume is proposed to represent the lung capacity distribution on the chest surface plane in this study. A convolutional neural network (CNN) is designed to implement lung segmentation and reconstruct 3D lung volume via CT scans. The 2D projection map is then compared to the recorded forced vital capacity (FVC) as a surrogate of tidal volume. The test was conducted against two CT datasets for training CNN model and build the 2D surface mapping. The results show a good correlation of R squared value of 0.71 between the overall lung capacity via the proposed 2D projection map against the FVC, thus showing the potential of back calculate the regional volume distribution using measured ventilator tidal volume and structured light surface motion.