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Automatic non-linear MRI-ultrasound registration for the correction of intra-operative brain deformations
Tal Arbel1, Xavier Morandi, Roch M Comeau
1Department of Electrical and Computer Engineering, Centre for Intelligent Machines, McGill University, Montréal, Québec, Canada. arbel@cim.mcgill.ca
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.
