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Investigation into the accuracy and measurement methods of sequential 3D dental scan alignment.

  • O'Toole, Saoirse1
  • Osnes, Cecilie2
  • Bartlett, David3
  • Keeling, Andrew4
  • 1 Department of Prosthodontics, King's College London Dental Institute, Floor 25, Tower Wing, Guy's Hospital, London SE19RT, UK. Electronic address: [email protected]
  • 2 Department of Medical Technologies, University of Siena, Siena, Italy; Department of Restorative Dentistry, Leeds School of Dentistry, Clarendon Way, Leeds LS2 9LU, UK. , (Italy)
  • 3 Centre for Clinical Oral and Translational Sciences, Faculty of Dental, Oral and Craniofacial Sciences, King's College London Dental Institute, Tower Wing, Guy's Hospital, London SE1 9RT, UK.
  • 4 Department of Restorative Dentistry, Leeds School of Dentistry, Clarendon Way, Leeds LS2 9LU, UK.
Published Article
Dental materials : official publication of the Academy of Dental Materials
Publication Date
Mar 01, 2019
DOI: 10.1016/
PMID: 30683418


Alignment procedures have yet to be standardised and may influence the measurement outcome. This investigation assessed the accuracy of commonly used alignment techniques and their impact on measurement metrics. Datasets of 10 natural molar teeth were created with a structured-light model-scanner (Rexcan DS2, Europac 3D, Crewe). A 300μm depth layer was then digitally removed from the occlusal surface creating a defect of known size. The datasets were duplicated, randomly repositioned and re-alignment attempted using a "best-fit" alignment, landmark-based alignment or reference alignment in Geomagic Control (3D Systems, Darmstadt, Germany). The re-alignment accuracy was mathematically assessed using the mean angular and translation differences between the original alignment and the re-aligned datasets. The effect of the re-alignment on conventional measurement metrics was calculated by analysing differences between the known defect size and defect size after re-alignment. Data were analysed in SPSS v24(ANOVA, post hoc Games Howell test, p<0.05). The mean translation error (SD) was 139μm (42) using landmark alignment, 130μm (26) for best-fit and 22μm (9) for reference alignment (p<0.001). The mean angular error (SD) between the datasets was 2.52 (1.18) degrees for landmark alignment, 0.56 (0.38) degrees for best-fit alignment and 0.26 (0.12) degrees for reference alignment (p<0.001). Using a reference alignment statistically reduced the mean profilometric change, volume change and percentage of surface change errors (p<0.001). Reference alignment produced significantly lower alignment errors and truer measurements. Best-fit and landmark-based alignment algorithms significantly underestimated the size of the defect. Challenges remain in identifying reference surfaces in a robust, clinically relevant method. Crown Copyright © 2019. Published by Elsevier Inc. All rights reserved.

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