Computational and Structural Biotechnology Journal (Dec 2024)

Source-detector trajectory optimization for FOV extension in dental CBCT imaging

  • S M Ragib Shahriar Islam,
  • Ander Biguri,
  • Claudio Landi,
  • Giovanni Di Domenico,
  • Benedikt Schneider,
  • Pascal Grün,
  • Cristina Sarti,
  • Ramona Woitek,
  • Andrea Delmiglio,
  • Carola-Bibiane Schönlieb,
  • Dritan Turhani,
  • Gernot Kronreif,
  • Wolfgang Birkfellner,
  • Sepideh Hatamikia

Journal volume & issue
Vol. 24
pp. 679 – 689

Abstract

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In dental imaging, Cone Beam Computed Tomography (CBCT) is a widely used imaging modality for diagnosis and treatment planning. Small dental scanning units are the most popular due to their cost-effectiveness. However, these small systems have the limitation of a small field of view (FOV) as the source and detector move at a limited angle in a circular path. This often limits the FOV size. In this study, we addressed this issue by modifying the source-detector trajectory of the small dental device. The main goal of this study was to extend the FOV algorithmically by acquiring projection data with optimal projection angulation and isocenter location rather than upgrading any physical parts of the device. A novel algorithm to implement a Volume of Interest (VOI) guided trajectory is developed in this study based on the small dental imaging device's geometry. In addition, this algorithm is fused with a previously developed off-axis scanning method which uses an elliptical trajectory, to compensate for the existing constraints and to further extend the FOV. A comparison with standard circular trajectory is performed. The FOV of such a standard trajectory is a circle of 11 cm diameter in the axial plane. The proposed novel trajectory extends the FOV significantly and a maximum FOV of 19.5 cm is achieved with the Structural Similarity Index Measure (SSIM) score ranging between (≈98-99%) in different VOIs. The study results indicate that the proposed source-detector trajectory can extend dental imaging FOV and increase imaging performance, which ultimately results in more precise diagnosis and enhanced patient outcomes.

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