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


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.
Arbel
Tal
T
Department of Electrical and Computer Engineering, Centre for Intelligent Machines, McGill University, Montréal, Québec, Canada. arbel@cim.mcgill.ca
Morandi
Xavier
X
Comeau
Roch M
RM
Collins
D Louis
DL
D001331:Automation; D001927:Brain Diseases / Q000175:diagnosis* / Q000601:surgery; D004453:Echoencephalography; D005240:Feasibility Studies; D006801:Humans; D008279:Magnetic Resonance Imaging; D008962:Models, Theoretical; D016343:Monitoring, Intraoperative / Q000379:methods*