A Brain-Deformation Framework Based on a Linear Elastic Model and Evaluation Using Clinical Data

Abstract

In image-guided neurosurgery, brain tissue displacement and deformation during neurosurgical procedures are a major source of error. In this paper, we implement and evaluate a linear-elastic-model-based framework for correction of brain shift using clinical data from five brain tumor patients. The framework uses a linear elastic model to simulate brain-shift behavior. The model is driven by cortical surface deformations, which are tracked using a surface-tracking algorithm combined with a laser-range scanner. The framework performance was evaluated using displacements of anatomical landmarks, tumor contours and self-defined evaluation parameters. The results show that tumor deformations predicted by the present framework agreed well with the ones observed intraoperatively, especially in the parts of the larger deformations. On average, a brain shift of 3.9 mm and a tumor margin shift of 4.2 mm were corrected to 1.2 and 1.3mm, respectively. The entire correction process was performed in less than 5 min. The data from this study suggest that the technique is a suitable candidate for intraoperative brain-deformation correction.