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

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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Publication Details

Journal
Computational and Structural Biotechnology Journal
Published
2024-11-08
DOI
https://doi.org/10.1016/j.csbj.2024.11.010
Citations
1
Primary Topic
Medical Imaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.40

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article

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

Wolfgang Birkfellner, Claudio Landi, Benedikt Schneider, Gernot Kronreif et al.
1 citations
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article

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

Wolfgang Birkfellner, Claudio Landi, Benedikt Schneider, Gernot Kronreif, Carola‐Bibiane Schönlieb, Giovanni Di Domenico, Pascal Grün, Sepideh Hatamikia, Dritan Turhani, Cristina Sarti, Ramona Woitek, Ander Biguri, Shuvo Islam, Andrea Delmiglio
article en
1 citations

Abstract

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.

Computational and Structural Biotechnology JournalVol. 24
Danube Private University (AT), University of Ferrara (IT), University of Cambridge (GB), Austrian Center for Medical Innovation and Technology (AT), Medical University of Vienna (AT)
Bundesministerium für Wissenschaft, Forschung und Wirtschaft, Bundesministerium für Verkehr, Innovation und Technologie, Austrian Center for Medical Innovation and Technology, Engineering and Physical Sciences Research Council
Good health and well-being
Openalex Percentile: Top 33%
Medical Imaging Techniques and Applications
0.40
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