A Surface Registration Method for Quantification of Intraoperative Brain Deformations in Image-Guided Neurosurgery

Intraoperative brain deformations decrease accuracy in image-guided neurosurgery. Approaches to quantify these deformations based on 3-D reconstruction of cortectomy surfaces have been described and have shown promising results regarding the extrapolation to the whole brain volume using additional prior knowledge or sparse volume modalities. Quantification of brain deformations from surface measurement requires the registration of surfaces at different times along the surgical procedure, with different challenges according to the patient and surgical step. In this paper, we propose a new flexible surface registration approach for any textured point cloud computed by stereoscopic or laser range approach. This method includes three terms: the first term is related to image intensities, the second to Euclidean distance, and the third to anatomical landmarks automatically extracted and continuously tracked in the 2-D video flow. Performance evaluation was performed on both phantom and clinical cases. The global method, including textured point cloud reconstruction, had accuracy within 2 mm, which is the usual rigid registration error of neuronavigation systems before deformations. Its main advantage is to consider all the available data, including the microscope video flow with higher temporal resolution than previously published methods.