The estimation of the volume of sheep mandibular defects using cone-beam computed tomography images and a stereological method

Dentomaxillofac Radiol. 2011 Mar;40(3):165-9. doi: 10.1259/dmfr/23067462.

Abstract

Objective: The Cavalieri principle of stereological methods is widely used to estimate the volume of structures. Recently in clinical practice, it has become common to use this approach for daily routine purposes. The Cavalieri principle provides quantitative and unbiased volume estimates which are independent of the observer. In the present study, the efficacy of using the Cavalieri principle to estimate the volume of sheep mandibular defects on cone beam CT (CBCT) scans was tested.

Methods: 24 differently sized defects were created on 4 sheep mandibles. Before the defects were created, the outer boundaries of the defects were determined using plaster casts. CBCT scans of the defects were taken. The scans were reconstructed in the coronal plane and sections of 0.2 mm thickness with 0.2 mm and 0.4 mm intervals were obtained. The volume of each defect was estimated using the Cavalieri principle. The models were created using light-body silicone for the estimation of the actual volume of each defect. They were immersed in water using a pycnometer and the actual volumes were obtained on the basis of the Archimedean principle. The actual and estimated volumes of the defects were compared using the Wilcoxon signed-rank test.

Results: The results showed that the volumes from the Cavalieri estimates did not differ from the actual volumes of the defects (P > 0.05).

Conclusion: We concluded that the volume of mandibular defects can be accurately estimated using the Cavalieri principle on images from a CBCT scan.

Publication types

  • Validation Study

MeSH terms

  • Algorithms
  • Animals
  • Cone-Beam Computed Tomography* / instrumentation
  • Image Processing, Computer-Assisted
  • Mandible / diagnostic imaging*
  • Mandible / pathology
  • Mandible / surgery*
  • Organ Size
  • Reproducibility of Results
  • Sheep
  • Statistics, Nonparametric