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Three-dimensional non-destructive visualization of teeth enamel microcracks using X-ray micro-computed tomography.

Irma Dumbryte1, Arturas Vailionis2,3, Edvinas Skliutas4

  • 1Vilnius University, Vilnius, Lithuania. i.dumbryte@gmail.com.

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This study validates X-ray micro-computed tomography (μCT) for 3D analysis of enamel microcracks (EMCs). The technique accurately visualizes EMCs, aiding in understanding their impact on tooth structures.

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Area of Science:

  • Dental Research
  • Biomaterials Science
  • Imaging Technology

Background:

  • Enamel microcracks (EMCs) are prevalent but their effect on underlying tooth structures is not fully understood.
  • Existing methods lack precision in evaluating the volumetric extent of EMC damage.
  • Three-dimensional (3D) analysis is crucial for a comprehensive understanding of EMCs.

Purpose of the Study:

  • To validate X-ray micro-computed tomography (μCT) for non-destructive 3D visualization and qualitative analysis of human teeth EMCs.
  • To assess the capability of μCT in detecting EMCs of varying severity, including those not visible on the surface.
  • To evaluate the utility of Deep Learning (DL) algorithms in segmenting and analyzing tooth structures in relation to EMCs.

Main Methods:

  • Extracted human maxillary premolars were analyzed using a ZEISS Xradia 520 Versa μCT instrument.
  • A Deep Learning (DL) algorithm, specifically a UNet neural network within Dragonfly software, was employed for segmentation of enamel, dentin, and cracks.
  • The μCT scanning technique was optimized for high-resolution 3D imaging.

Main Results:

  • The μCT technique successfully detected and visualized EMCs, including those hidden deep within the tooth structure.
  • DL-assisted segmentation enabled precise differentiation between enamel, dentin, and crack volumes.
  • 3D visualization facilitated the evaluation of EMC dynamics and their precise location relative to the dentin-enamel junction.

Conclusions:

  • X-ray micro-computed tomography (μCT) is a validated and effective technique for the 3D non-destructive analysis of enamel microcracks (EMCs).
  • The combination of μCT and Deep Learning (DL) segmentation provides accurate insights into EMC severity, depth, and structural implications.
  • This approach enhances the understanding of dental damage and supports precise clinical evaluation.