Accuracy and Reproducibility of Voxel Based Superimposition of Cone Beam Computed Tomography Models on the Anterior Cranial Base and the Zygomatic Arches

Superimposition of serial Cone Beam Computed Tomography (CBCT) scans has become a valuable tool for three dimensional (3D) assessment of treatment effects and stability. Voxel based image registration is a newly developed semi-automated technique for superimposition and comparison of two CBCT scans. The accuracy and reproducibility of CBCT superimposition on the anterior cranial base or the zygomatic arches using voxel based image registration was tested in this study. 16 pairs of 3D CBCT models were constructed from pre and post treatment CBCT scans of 16 adult dysgnathic patients. Each pair was registered on the anterior cranial base three times and on the left zygomatic arch twice. Following each superimposition, the mean absolute distances between the 2 models were calculated at 4 regions: anterior cranial base, forehead, left and right zygomatic arches. The mean distances between the models ranged from 0.2 to 0.37 mm (SD 0.08–0.16) for the anterior cranial base registration and from 0.2 to 0.45 mm (SD 0.09–0.27) for the zygomatic arch registration. The mean differences between the two registration zones ranged between 0.12 to 0.19 mm at the 4 regions. Voxel based image registration on both zones could be considered as an accurate and a reproducible method for CBCT superimposition. The left zygomatic arch could be used as a stable structure for the superimposition of smaller field of view CBCT scans where the anterior cranial base is not visible

Outcome measures of group B (studies focused on impacted teeth).

<p>ortho = orthodontist(s); pgs = postgraduate(s); dent = dentist(s); surg = dental surgeon; LHP = lateral headplate; OPT = orthopantomogram; CT = computer tomography; CBCT = cone beam computed tomography; MSCT = multi-slice computed tomography; 2D = two-dimensional.</p

Outcome measures of group A (studies not focused on impacted teeth).

<p>ortho = orthodontist(s); pgs = postgraduate(s); dent = dentist(s); period = periodontist(s); admini = administrative personnel; LHP = lateral headplate; OPT = orthopantomogram; 2D = two-dimensional.</p

PRISMA flow diagram of study selection process.

<p>PRISMA flow diagram of study selection process.</p

Results of the quality assessment of the included studies using the QUADAS-2 instrument.

<p>Yes = Low Risk.</p><p>No = High Risk.</p><p>? = Unclear Risk.</p

Proportion of studies with low, high, or unclear characteristics regarding ‘risk of bias.’

<p>Proportion of studies with low, high, or unclear characteristics regarding ‘risk of bias.’</p

Pubmed search strategy.

<p>Pubmed search strategy.</p

Proportion of studies with low, high, or unclear characteristics regarding ‘applicability concerns.’

<p>Proportion of studies with low, high, or unclear characteristics regarding ‘applicability concerns.’</p

Description of the Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS) −2 [8].

<p>Description of the Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS) −2 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074186#pone.0074186-Whiting1" target="_blank">[8]</a>.</p

The influence of the model superimposition method on the assessment of accuracy and predictability of setup models.

INTRODUCTION The aim of this study was to evaluate the influence of different superimposition methods on the accuracy and predictability of conventional and virtual diagnostic setups. MATERIALS AND METHODS Ten finished cases were used to make a conventional setup and a virtual setup. Second molars were not moved in the two setup situations to allow a reference for superimposition. Conventional and virtual setups were superimposed and compared by second molar registration and the whole surface best fit method (WSBF). Conventional and virtual setups were compared to the posttreatment models with WSBF and palatal rugae best fit (PRBF). Anterior, intermediate, and posterior regions of the dental arches were compared. The paired t-test was used to compare the mean differences between conventional and virtual setups, posttreatment models and both conventional and virtual setups by the WSBF method, and between maxillary posttreatment and virtual setup models using the WSBF and PRBF methods. RESULTS Conventional and virtual setups differed depending on the two superimposition methods used. Superimposition of the posttreatment models and both setups using WSBF presented no statistically significant differences. There were statistically significant differences between posttreatment and virtual setup models using WSBF and PRBF superimposition methods. CONCLUSIONS The model superimposition method influenced the assessment of accuracy and predictability of setup models. There were statistically significant differences between the maxillary posttreatment and virtual setup models using the WSBF and the PRBF superimposition methods. It is important to establish stable structures to evaluate the accuracy and predictability of setup models