A data fusion environment for multimodal and multi-informational neuronavigation.

OBJECTIVE: Part of the planning and performance of neurosurgery consists of determining target areas, areas to be avoided, landmark areas, and trajectories, all of which are components of the surgical script. Nowadays, neurosurgeons have access to multimodal medical imaging to support the definition of the surgical script. The purpose of this paper is to present a software environment developed by the authors that allows full multimodal and multi-informational planning as well as neuronavigation for epilepsy and tumor surgery. MATERIALS AND METHODS: We have developed a data fusion environment dedicated to neuronavigation around the Surgical Microscope Neuronavigator system (Carl Zeiss, Oberkochen, Germany). This environment includes registration, segmentation, 3D visualization, and interaction-applied tools. It provides the neuronavigation system with the multimodal information involved in the definition of the surgical script: lesional areas, sulci, ventricles segmented from magnetic resonance imaging (MRI), vessels segmented from magnetic resonance angiography (MRA), functional areas from magneto-encephalography (MEG), and functional magnetic resonance imaging (fMRI) for somatosensory, motor, or language activation. These data are considered to be relevant for the performance of the surgical procedure. The definition of each entity results from the same procedure: registration to the anatomical MRI data set (defined as the reference data set), segmentation, fused 3D display, selection of the relevant entities for the surgical step, encoding in 3D surface-based representation, and storage of the 3D surfaces in a file recognized by the neuronavigation software (STP 3.4, Leibinger; Freiburg, Germany). RESULTS: Multimodal neuronavigation is illustrated with two clinical cases for which multimodal information was introduced into the neuronavigation system. Lesional areas were used to define and follow the surgical path, sulci and vessels helped identify the anatomical environment of the surgical field, and, finally, MEG and fMRI functional information helped determine the position of functional high-risk areas. CONCLUSION: In this short evaluation, the ability to access preoperative multi-functional and anatomical data within the neuronavigation system was a valuable support for the surgical procedure.

[Sulcal identification and neuronavigation in supratentorial cavernoma surgery]. / Repérage sulcal et neuronavigation dans la chirurgie des cavernomes supratentoriels.

We present the use of cortical sulci, segmented from magnetic resonance imaging, in image guided neurosurgery. Sulcal information was transferred to a surgical microscope with enhanced reality features. This assistance was used for the resection of supratentorial cavernomas (7 patients). Sulci were semi-automatically segmented from 3D MRI data sets. Sulci close to the cavernoma were selected and transferred to the neuronavigation system which allows the superimposition of graphics into the right ocular of the microscope. Selected sulci were displayed on the workstation and superimposed into the ocular of the microscope. Cortical sulci proved to be useful for the recognition of the anatomical environment. The superimposed sulci helped to optimize location and size of the skin incision as well as to guide the access to the cavernoma by using the course of a sulcus as indirect trajectory.

Stereo augmented reality in the surgical microscope.

We present an augmented reality system that allows surgeons to view features from preoperative radiological images accurately overlaid in stereo in the optical path of a surgical microscope. The purpose of the system is to show the surgeon structures beneath the viewed surface in the correct 3-D position. The technical challenges are registration, tracking, calibration and visualisation. For patient registration, or alignment to preoperative images, we use bone-implanted markers and a dental splint is used for patient tracking. Both microscope and patient are tracked by an optical localiser. Calibration uses an accurately manufactured object with high contrast circular markers which are identified automatically. All ten camera parameters are modelled as a bivariate polynomial function of zoom and focus. The overall system has a theoretical overlay accuracy of better than 1 mm. Implementations of the system have been tested on seven patients. Recent measurements in the operating room conformed to our accuracy predictions. For visualisation the system has been implemented on a graphics workstation to enable high frame rates with a variety of rendering schemes. Several issues of 3-D depth perception remain unsolved, but early results suggest that perception of structures in the correct 3-D position beneath the viewed surface is possible.