Automatic virtual reconstruction of maxillofacial bone defects assisted by ICP (iterative closest point) algorithm and normal people database.

objectivesThe aim of this study was to propose and validate an automatic approach based on iterative closest point algorithm for virtual complement and reconstruction for maxillofacial bone defects. MATERIALS AND

methodsA 3D craniomaxillofacial database of normal Chinese people including 500 skull models was established. Modified iterative closest point (ICP) algorithm was developed to complete bone defects automatically. The performances were evaluated by two approaches: (1) model experiment, virtual bony defects were created on 30 intact normal skull models not included in the database. For each defect model, the algorithm was applied to select the reference skull model from the database. 3-Dimensional and 2-dimensional comparison were conducted to evaluate the error between reference skull model with original intact model. Root mean square error (RMSE) and processing time were calculated. (2) Clinical application, the algorithm was utilized to assist reconstruction of 5 patients with maxillofacial bone defects. The symmetry of post-operative skull model was evaluated by comparing with its mirrored model.

resultsThe algorithm was tested on an CPU with 1.80 GHz and average processing time was 493.5 s. (1) Model experiment, the average root-mean-square deviation of defect area was less than 2 mm. (2) Clinical application, the RMSE of post-operative skull and its mirrored model was 1.72 mm.

conclusionIt is feasible using iterative closest point algorithm based on normal people database to automatically predict the reference data of missing maxillofacial bone. CLINICAL RELEVANCE: An automated approach based on ICP algorithm and normal people database for maxillofacial bone defect reconstruction has been proposed and validated.