Viscoelastic biomechanical models to predict inward brain-shift using public benchmark data.

Brain-shift during neurosurgery compromises the accuracy of tracking the boundaries of the tumor to be resected. Although several studies have used various finite element models (FEMs) to predict inward brain-shift, evaluation of their accuracy and efficiency based on public benchmark data has been limited. This study evaluates several FEMs proposed in the literature (various boundary conditions, mesh sizes, and material properties) by using intraoperative imaging data (the public REtroSpective Evaluation of Cerebral Tumors [RESECT] database). Four patients with low-grade gliomas were identified as having inward brain-shifts. We computed the accuracy (using target registration error) of several FEM-based brain-shift predictions and compared our findings. Since information on head orientation during craniotomy is not included in this database, we tested various plausible angles of head rotation. We analyzed the effects of brain tissue viscoelastic properties, mesh size, craniotomy position, CSF drainage level, and rigidity of meninges and then quantitatively evaluated the trade-off between accuracy and central processing unit time in predicting inward brain-shift across all models with second-order tetrahedral FEMs. The mean initial target registration error (TRE) was 5.78 ± 3.78 mm with rigid registration. FEM prediction (edge-length, 5 mm) with non-rigid meninges led to a mean TRE correction of 1.84 ± 0.83 mm assuming heterogeneous material. Results show that, for the low-grade glioma patients in the study, including non-rigid modeling of the meninges was significant statistically. In contrast including heterogeneity was not significant. To estimate the optimal head orientation and CSF drainage, an angle step of 5° and an CSF height step of 5 mm were enough leading to <0.26 mm TRE fluctuation.

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 computed tomography (CT) scan. The feasibility of the proposed approach is evaluated using preliminary data from six 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 shear modulus. For five patients, the minimum achieved mean TLE was inferior to 9 mm using patient-specific parameters and inferior to 10 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.