Intraoperative cortical surface characterization using laser range scanning: preliminary results.

Image-guided neurosurgical techniques have been developed to provide additional spatial reference during surgery. In the past, these positional cues have been provided by correlating a three-dimensional (3D) digitized point within patient space (touching the brain surface with a stylus) to the corresponding point within image space (usually simultaneously rendered on three orthogonal/cardinal slices and one isometric view of the preoperative image volume). Complementary technologies have been created to improve landmark recognition, which include multimodal image fusion, modifications to operating microscopes, virtual head-mounted displays, and enhanced-reality visualization, among others (4, 6–10, 12, 16, 25, 31, 33). Although an impressive myriad of technology has been developed, there has been little quantitative documented benefit with respect to outcome or improved surgical performance. Whereas texturing laser range scanning (tLRS) technology could be seen as another in a list of these innovations, tLRS may have greater potential in that it may facilitate compensation for intraoperative tissue deformation during image-guided surgery (IGS), i.e., the brain shift problem (18, 20). Combined with the low cost of laser optics and charge-coupling device (CCD) arrays, tLRS technology presents an interesting adjunct for IGS work. In brief, a tLRS system passes a laser stripe over the surface of interest while collecting the reflected light with a high-resolution CCD camera. Through the process of triangulation, the data acquired can be interpreted to reconstruct the 3D surface as a cloud of digitized points. The “texturing” nomenclature refers to a relatively novel feature whereby a second digital image of the field of view (FOV) is acquired at the time of scanning and is subsequently mapped to the 3D geometric point cloud. As a result, a representation of data is captured that contains both geometric and intensity information, i.e., a “textured” brain surface, the shape and visual features of which are associated with the FOV. Textured surfaces can provide many cues that aid in object recognition and localization, including information about depth, curvature, the orientation of surfaces, and distances between object features (23). For example, the benefit of adding texture to shape information in the field of face recognition is well established (3, 21, 22, 29). Except for some limited studies using vessel bifurcations for image-to-patient registration and point-based brain shift measurement (19, 24), cortical surface texture methods have not been studied with respect to neurosurgical navigation. Nonetheless, several findings from face recognition research may apply to neuronavigation. One of the most important of these is that the recognition speed of features can be enhanced by texture. Similarly, the orientation of the textured surface is important and it may be favorable to use tLRS displays that are reflective of an egocentric reference relevant to the viewer (29, 30). Furthermore, animation and dynamic viewing may be important enhancements for recognition. With respect to visualizing brain shift, it may be more effective for a surgeon to see a dynamic rendering of the tLRS cortical surface to appreciate the degree of motion (11, 13). Given the localized, feature-rich nature of the brain surface (i.e., non-diffuse features), the use of tLRS data should reduce view-point dependencies. Despite the abundance of recognition research performed, previous work does not entirely apply to IGS visualization. The alignment of cortical tLRS data to a segmented magnetic resonance (MR) grayscale encoded volume rendering of the brain represents two completely different visual modalities. The difference in the geometric and textural representation of these surfaces requires an inherent pattern recognition task that is significantly different. Another dissimilarity is that recognition experiments often involve a learning and testing phase in which the recognition objects contain the same localized features (e.g., geometric structures of subjects do not change, only their pose). In IGS, the exposed cortical surface cannot be visualized before surgery and is markedly dissimilar in texture and appearance than the MR image counterpart (which can be studied preoperatively). This represents a fundamentally more challenging feature recognition task. However, this difficulty may be significantly offset by the level of expertise in the testing population (neurosurgeons). We conducted a clinical study to assess the extent to which tLRS technology can be used in the operative setting and report the utility of this unique data and the enhanced visualization it provides. We examined two points in this preliminary, prospective clinical study. First, we quantitatively examined a series of image-to-patient registrations in near real-time to assess the fidelity of this registration process. Second, we investigated whether our novel tLRS-to-MR image volume displays could enhance navigational and cortical surface recognition. Our findings suggest that tLRS technology may allow surgeons to better evaluate the fidelity of image-to-patient registration and that tLRS can accurately provide anatomic cues for cortical recognition assistance, which will provide for real-time assessment of intraoperative brain shift.