Automatic non-linear MRI-ultrasound registration for the correction of intra-operative brain deformations

Abstract

Objective: Movements of brain tissue during neurosurgical procedures reduce the effectiveness of using pre-operative images for intra-operative surgical guidance. In this paper, we explore the use of acquiring intra-operative ultrasound (US) images for the quantification of and correction for non-linear brain deformations.

Materials and Methods: We will present a multi-modal registration strategy that automatically matches pre-operative images (e.g., MRI) to intra-operative US to correct for these deformations. The strategy involves using the predicted appearance of neuroanatomical structures in US images to build “pseudo ultrasound” images based on pre-operative segmented MRI. These images can then be non-linearly registered to intra-operative US using cross-correlation measurements within the ANIMAL package. The feasibility of the theory is demonstrated through its application to clinical patient data acquired during 12 neurosurgical procedures.

Results: Results of applying the method to 12 surgical cases, including those with brain tumors and selective amygdalo-hippocampectomies, indicate that our strategy significantly recovers from non-linear brain deformations occurring during surgery. Quantitative results at tumor boundaries indicate up to 87% correction for brain shift.

Conclusions: Qualitative and quantitative examination of the results indicate that the system is able to correct for non-linear brain deformations in clinical patient data.

• image-guided neurosurgery
• ultrasound
• magnetic resonance imaging
• non-linear registration
• brain shift