BACKGROUND AND AIM: The prediction of soft tissue esthetics is important for achieving an optimal esthetic outcome in orthodontic treatment planning. Applicable procedures have so far been restricted to two-dimensional profile predictions that have not proven to be very reliable. The goal of this investigation was therefore to develop a novel finite element-based procedure that allows a three-dimensional, easily visualized, quantitative analysis and prediction of soft tissue behavior for the clinician. The procedure to be developed should be easy to handle and not entail any additional radiation exposure for the patient. MATERIAL AND METHODS: Using a three-dimensional scanner, the facial surfaces of 20 probands were digitalized and individual FEM models were generated. RESULTS: After reduction of data redundancy via several conversion steps, a patient-specific simulation model was prepared consisting of 20,000 to 40,000 individual elements to which specific physical properties could be assigned. The average time required for generating a virtual model was 50 minutes. Problems occurring during model generation were rare (mainly shadowing phenomena and movement artifacts). CONCLUSION: The procedure outlined herein makes the reliable generation of patient-specific simulation models possible for facial soft tissue prediction in orthodontics.
BACKGROUND AND AIM: The prediction of soft tissue esthetics is important for achieving an optimal esthetic outcome in orthodontic treatment planning. Applicable procedures have so far been restricted to two-dimensional profile predictions that have not proven to be very reliable. The goal of this investigation was therefore to develop a novel finite element-based procedure that allows a three-dimensional, easily visualized, quantitative analysis and prediction of soft tissue behavior for the clinician. The procedure to be developed should be easy to handle and not entail any additional radiation exposure for the patient. MATERIAL AND METHODS: Using a three-dimensional scanner, the facial surfaces of 20 probands were digitalized and individual FEM models were generated. RESULTS: After reduction of data redundancy via several conversion steps, a patient-specific simulation model was prepared consisting of 20,000 to 40,000 individual elements to which specific physical properties could be assigned. The average time required for generating a virtual model was 50 minutes. Problems occurring during model generation were rare (mainly shadowing phenomena and movement artifacts). CONCLUSION: The procedure outlined herein makes the reliable generation of patient-specific simulation models possible for facial soft tissue prediction in orthodontics.
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Authors: Gregor F Raschke; Ulrich M Rieger; Andre Peisker; Gabriel Djedovic; Marta Gomez-Dammeier; Arndt Guentsch; Oliver Schaefer; Stefan Schultze-Mosgau Journal: Med Oral Patol Oral Cir Bucal Date: 2015-01-01
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