PURPOSE: The utilization of three-dimensional modeling technology in craniomaxillofacial surgery has grown exponentially during the last decade. Future development, however, is hindered by the lack of a normative three-dimensional anatomic dataset and a statistical mean three-dimensional virtual model. The purpose of this study is to develop and validate a protocol to generate a statistical three-dimensional virtual model based on a normative dataset of adult skulls. METHOD: Two hundred adult skull CT images were reviewed. The average three-dimensional skull was computed by processing each CT image in the series using thin-plate spline geometric morphometric protocol. Our statistical average three-dimensional skull was validated by reconstructing patient-specific topography in cranial defects. The experiment was repeated 4 times. In each case, computer-generated cranioplasties were compared directly to the original intact skull. The errors describing the difference between the prediction and the original were calculated. RESULTS: A normative database of 33 adult human skulls was collected. Using 21 anthropometric landmark points, a protocol for three-dimensional skull landmarking and data reduction was developed and a statistical average three-dimensional skull was generated. Our results show the root mean square error (RMSE) for restoration of a known defect using the native best match skull, our statistical average skull, and worst match skull was 0.58, 0.74, and 4.4 mm, respectively. CONCLUSIONS: The ability to statistically average craniofacial surface topography will be a valuable instrument for deriving missing anatomy in complex craniofacial defects and deficiencies as well as in evaluating morphologic results of surgery.
PURPOSE: The utilization of three-dimensional modeling technology in craniomaxillofacial surgery has grown exponentially during the last decade. Future development, however, is hindered by the lack of a normative three-dimensional anatomic dataset and a statistical mean three-dimensional virtual model. The purpose of this study is to develop and validate a protocol to generate a statistical three-dimensional virtual model based on a normative dataset of adult skulls. METHOD: Two hundred adult skull CT images were reviewed. The average three-dimensional skull was computed by processing each CT image in the series using thin-plate spline geometric morphometric protocol. Our statistical average three-dimensional skull was validated by reconstructing patient-specific topography in cranial defects. The experiment was repeated 4 times. In each case, computer-generated cranioplasties were compared directly to the original intact skull. The errors describing the difference between the prediction and the original were calculated. RESULTS: A normative database of 33 adult human skulls was collected. Using 21 anthropometric landmark points, a protocol for three-dimensional skull landmarking and data reduction was developed and a statistical average three-dimensional skull was generated. Our results show the root mean square error (RMSE) for restoration of a known defect using the native best match skull, our statistical average skull, and worst match skull was 0.58, 0.74, and 4.4 mm, respectively. CONCLUSIONS: The ability to statistically average craniofacial surface topography will be a valuable instrument for deriving missing anatomy in complex craniofacial defects and deficiencies as well as in evaluating morphologic results of surgery.