Automatically Generating Subject-specific Functional Tooth Surfaces Using Virtual Mastication

Abstract

High-accuracy geometrical models of a subject's mandibular and maxillary teeth are combined with recordings of natural chewing trajectories of the same subject to obtain a subject-specific virtual model of mastication-the virtual masticator. The virtual masticator and a shape-optimization algorithm, which is based on removing collisions occurring between a generic maxillary tooth/teeth and the mandibular antagonists during mastication, is used to automatically reconstruct functional tooth surfaces. The process was tested using a chewing trajectory stemming from recordings made of an individual while eating elastic-type foods, a generic maxillary tooth, and the mandibular second molar of that individual. Comparing the obtained results with the actual tooth, the errors within the occlusal and functional regions of the the right second maxillary molar range between -90 and 200 mum and these errors do not change any more after three chewing cycles. These results indicate that a small number of chewing cycles is sufficient to remove occlusal interferences in the virtual tooth model. Such automatically reconstructed tooth surfaces can provide guidance during the design stage of dental fixed restorations manufactured using computer-aided design and manufacturing (CAD/CAM) systems and provide additional information for the design of dental implants or bridges.

The Use of Collision Detection to Infer Multi-Camera Calibration Quality

“…This problem of collision detection could also be viewed as a matter of shape optimization (Saini et al, 2009 ). Figures 4 A–C show the simplified 2D view of the adjusted shape of C T at time t 0 , t i and t j with 0 < i < j.…”

“…In this study, we have developed a procedure for quantifying calibration accuracy of a multi-camera system based on collision detection by using markers that move relatively to each other during the calibration procedure. Saini et al ( 2009 ) had previously utilized this procedure. In their study, the geometry of a mandibular (lower) tooth obtained from computed tomography (CT) and motion capture data of natural chewing movements were used to automatically reconstruct the tooth’s maxillary (upper) counterpart.…”

Comparison between Occlusal Errors of Single Posterior Crowns Adjusted Using Patient Specific Motion or Conventional Methods

“…The result indicated an easier occlusal adjustment in the FGP single crown, although the clinically relevant differences were small. In another study, Saini et al [47] studied an occlusal surface that was designed using a virtual mastication simulator. Comparing the obtained results with an actual tooth, these subject-specific functional occlusal surfaces displayed errors in functional and static occlusion, which ranged between 90 and 200 µm.…”

“…In the PSM group, not all occlusal errors could be reduced because of inaccuracies caused by various factors. Some studies have reported that the accuracy of a mandibular motion tracking system is 0.2-0.3 mm [7,47]. In addition, movement in loaded teeth and adjacent teeth cannot be recorded with an optical tracking device [21].…”

Current computational modelling trends in craniomandibular biomechanics and their clinical implications

“…Saini et al. (50) describe an automatic method that combines high‐resolution digital dental models, subject‐specific masticatory motion sequences and shape optimisation to prevent unwanted collisions between opposing virtual teeth and to guide the occlusal design of fixed prostheses.…”