ETherm3 is a finite-element software suite for simulations of electrosurgery and RF thermal ablation processes. Program components cover the complete calculation process from mesh generation to solution analysis. The solutions employ three-dimensional conformal meshes to handle cluster probes and other asymmetric assemblies. The conformal-mesh approach is essential for high-accuracy surface integrals of net electrode currents. ETherm3 performs coupled calculations of RF electric fields in conductive dielectrics and thermal transport via dynamic solutions of the bioheat equation. The boundary-value RF field solution is updated periodically to reflect changes in material properties. ETherm3 features advanced material models with the option for arbitrary temperature variations of thermal and electrical conductivity, perfusion rate, and other quantities. The code handles irreversible changes by switching the material reference of individual elements at specified transition temperatures. ETherm3 is controlled through a versatile interpreter language to enable complex run sequences. The code can automatically maintain constant current or power, switch to different states in response to temperature or impedance information, and adjust parameters on the basis of user-supplied control functions. In this paper, we discuss the physical basis and novel features of the code suite and review application examples.
KEYWORDS: Brain, Surgery, Ultrasonography, Neuroimaging, Magnetic resonance imaging, Data modeling, Tissues, Human-machine interfaces, Finite element methods, Head
Image-guided neurosurgery typically relies on preoperative imaging information that is subject to errors resulting from brain shift and deformation in the OR. A graphical user interface (GUI) has been developed to facilitate the flow of data from OR to image volume in order to provide the neurosurgeon with updated views concurrent with surgery. Upon acquisition of registration data for patient position in the OR (using fiducial markers), the Matlab GUI displays ultrasound image overlays on patient specific, preoperative MR images. Registration matrices are also applied to patient-specific anatomical models used for image updating. After displaying the re-oriented brain model in OR coordinates and digitizing the edge of the craniotomy, gravitational sagging of the brain is simulated using the finite element method. Based on this model, interpolation to the resolution of the preoperative images is performed and re-displayed to the surgeon during the procedure. These steps were completed within reasonable time limits and the interface was relatively easy to use after a brief training period. The techniques described have been developed and used retrospectively prior to this study. Based on the work described here, these steps can now be accomplished in the operating room and provide near real-time feedback to the surgeon.
Patient registration, a key step in establishing image guidance, has to be performed in real-time after the patient is anesthetized in the operating room (OR) prior to surgery. We propose to use cortical vessels as landmarks for registering the preoperative images to the operating space. To accomplish this, we have attached a video camera to the optics of the operating microscope and acquired a pair of images by moving the scope. The stereo imaging system is calibrated to obtain both intrinsic and extrinsic camera parameters. During neurosurgery, right after opening of dura, a pair of stereo images is acquired. The 3-D locations of blood vessels are estimated via stereo vision techniques. The same series of vessels are localized in the preoperative image volume. From these 3-D coordinates, the transformation matrix between preoperative images and the operating space is estimated. Using a phantom, we have demonstrated that patient registration from cortical vessels is not only feasible but also more accurate than using conventional scalp-attached fiducials. The Fiducial Registration Error (FRE) has been reduced from 1 mm using implanted fiducials to 0.3 mm using cortical vessels. By replacing implanted fiducials with cortical features, we can automate the registration procedure and reduce invasiveness to the patient.
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