A Comparative Investigation of Multi-Source and Clinical Cone Beam Computed Tomography in Predicting Dental Implant Stability with Porcine Model
Year: 2025
Type: dissertation
Abstract: Accurate assessment of bone quality is pivotal for successful dental implant therapy, where primary stability, commonly indicated by insertion torque, serves as a critical predictor of implant longevity. This study evaluated whether a novel multisource cone beam computed tomography (ms-CBCT) design offers a more accurate correlation between bone density—approximated by Hounsfield Unit (HU) values—and implant insertion torque compared to conventional single-source CBCT for implant planning. A total of 12 dental implant sites were imaged by both ms-CBCT and conventional CBCT. HU values were calculated by averaging voxel values in the vicinity of each planned implant location. Insertion torque was recorded intraoperatively using a digital torque wrench. Correlation analyses (Pearson’s r) assessed the strength of the association between insertion torque and HU values from each imaging modality. ms-CBCT-derived HU showed a higher and statistically significant correlation with insertion torque (R2 = 0.8306, p = 1.42E-5) than that of the conventional CBCT (R2 = 0.5524, p = 0.0056). However, the inter-group t-test did not reach statistical significance (p = 0.146), likely due to the limited sample size. These findings nonetheless suggest that ms-CBCT may better approximate local bone quality relevant to primary implant stability. By mitigating scatter and cupping beam artifacts, ms-CBCT enhances HU accuracy and achieves a stronger correlation with implant insertion torque. These findings highlight ms-CBCT’s clinical potential to improve patient-specific implant planning, reduce failure rates, and refine treatment protocols in implant dentistry.
DOI: 10.17615/rv02-nv51
Author: Luo, Wei
Institution: University of North Carolina at Chapel Hill
Language: English
Subfield: Oral Surgery
Field: Dentistry
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W6925327002
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