A Three-Dimensional Statistical Average Skull: Application of Biometric Morphing in Generating Missing Anatomy.

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.