Preliminary evaluation of biomechanical modeling of lung deflation during minimally invasive surgery using pneumothorax computed tomography scans.

During minimally invasive surgery (MIS) for lung tumor resection, the localization of tumors or nodules relies on visual inspection of the deflated lung on intra-procedural video. For patients with tumors or nodules located deeper in the lung, this localization is not possible without prior invasive marking techniques. In efforts to avoid the increase of complication rates associated with these invasive techniques, this study investigates the use of biomechanical modeling of the lung deflation to predict the tumor localization during MIS, solely based on a pre-operative CT scan. The feasibility of the proposed approach is evaluated using preliminary data from four patients who presented with pneumothorax after lung biopsy and underwent chest tube insertion. For each patient, a hyperelastic finite-element model of the lung was created from the CT scan showing the re-inflated lung. Boundary conditions were applied on the lung surface to simulate the gravity and insufflation of carbon dioxide in the chest. The impact of adding rigid constraints around the main airway was also evaluated. To evaluate the accuracy of the model in predicting lung tissues or potential tumor displacement, at least five corresponding landmarks were identified for each patient in the CT scans of their deflated and re-inflated lungs. Using these landmarks, target localization errors (TLE) were measured for different sets of pressure applied to lung surface and ground shear modulus. For three patients, while the initial mean distance between the landmarks was superior to 20 mm, the minimum achieved mean TLE was inferior to 8 mm using patient-specific parameters and equal or inferior to 9 mm using the same parameterization. The predicted and ground truth deflated lung surfaces presented visually a relatively good agreement. The proposed approach thus appears as a promising tool for integration in future lung surgery image-guidance systems.