We have presented a method for registration of electrode positions from standard medical images. In summary, pre-implant and post-implant digital photographs are registered with each other via a projective transform. Next, the pre-implant photograph, now annotated with the visible electrode positions, is used to manually annotate the corresponding anatomy on an MRI rendering of the brain. These visible electrodes are then used to compute another projective transform between the MRI and the radiograph. Finally, backprojection using this transform reveals the locations of occluded electrodes.
We have demonstrated that our procedure outputs an accurate localization of electrode positions and does not require additional imaging procedures that add risk, radiation exposure, and expense to standard practice. Our technique makes use of digital photographs and high-resolution MRIs that have only recently been incorporated into the standard of care for epilepsy surgery patients. Precise knowledge of the X-ray configuration, placement of additional fiducial markers on the patient, and special calibration are not required. Most importantly, as the input images required are already collected by most neurosurgery centers, the technique can be quickly implemented and introduced to clinical use as well as applied to archived images from past patients for research use.
We have also shown that a popular technique for electrode localization, coregistration of postimplant CT scans with preoperative MRI scans, may not provide accurate results. The thicker slices usually obtained with standard clinical CT scanners result in significant partial volume averaging and coarse resolution along the superior-inferior axis. Furthermore, coregistrations may be unreliable since simply performing a craniotomy is known to significantly deform brain tissue (Hill et al., 1998
; Roberts et al., 1998
); particularly for patients implanted with chronic electrodes, swelling and the thickness of the electrodes along with associated hardware can contribute to further brain tissue displacement relative to the preoperative MRI. While the same issues may arise with our technique since we are also registering postimplant images with preoperative structure, we expect their impact to be less significant since the algorithm fits only the portion of the brain covered by electrodes and incorporates highly reliable information from the surgical photographs. Therefore, the various projective transforms involved may effectively absorb some brain deformation.
We have applied our method to register superficial electrodes, for which the estimated solution is the intersection of the X-ray backprojection with the cortical surface. The localization of deep electrode arrays can also be accomplished if the depth of the shallowest electrode contact is used to choose the displacement along the backprojected line from the cortical surface. The remaining electrodes can then be computed similarly using known electrode spacing. If additional X-ray views are available, depth electrode locations may instead be localized by computing separate projective transforms for each X-ray. The solution would then be the intersection of the multiple backprojected lines for a given electrode.
While having a full set of high-quality image data (photographs, MRI, and radiograph) increases confidence in the results, the presented technique is still useful if some images are poor or missing altogether. As anatomy is still identifiable through the Silastic electrode sheet in a good-quality post-implant photograph, the pre-implant photograph is not strictly necessary, though it does ease the procedure to have it. Without a radiograph, MRI-photograph registration can be performed, and the position of hidden electrodes can be estimated from known electrode spacing and brain curvature (e.g., see results for GP7 shown in ). MRI-radiograph coregistration can still be performed in the absence of surgical photographs with an estimate of a few electrode positions on the MRI and the use of other anatomical features as control points. (This last scenario likely would not provide results better than CT-MRI registration, but provides an option in the event of an archived patient dataset in which no photographs or CT are available.) Finally, at any point in the process, electrode locations can be manually adjusted to account for information from other sources such as known electrode spacing, CT coregistration, or coordinates from surgical navigation devices.
One metric to evaluate the quality of the solution for hidden electrodes in individual patients would be to examine the spacing of estimated electrode locations. Since electrode spacing is generally known, a large disagreement would suggest a suboptimal solution. In this event, however, the electrode spacing can then be used to constrain the solution and provide a satisfactory result, even in regions of high brain convexity where a small error in X-ray coordinates can result in large backprojection errors.
Much of the time required for our procedure was spent simply converting between different image formats and manually transcribing coordinates; manual annotation of visible electrodes and landmarks on the various images also consumed a large proportion of time. However, with further development, all required procedures can be implemented as an integrated software package, and it should be possible to automate the image annotation procedures. We expect this could reduce overall rendering time to less than 2 hours per patient.
The algorithm presented is a valuable complement to existing medical imaging software that fuses different imaging modalities (e.g., existing software coregisters MRI and CT or MRI and PET). It would be useful as part of a suite of programs specifically intended for the diagnostic imaging and monitoring of neurosurgery patients with implanted electrodes. We have found that our interactive navigation tool to be clinically useful to aid in the positive identification of structures of interest on the X-ray and photograph; it could also be suitable in an educational setting to help students connect neuroanatomy with radiographs, photographs, MRI slices, and MRI renderings.