Abstract
Purpose
This ex vivo study was performed to determine the optimal energy level for virtual monoenergetic images (VMIs) generated with photon-counting detector computed tomography (PCD-CT) to minimize metal artifacts from dental implants.
Materials and Methods
Twelve implants from various manufacturers were placed in 6 pig mandibles and scanned with PCD-CT. VMIs were reconstructed at energy levels from 70 keV to 150 keV in 20-keV increments. Three readers with varying experience qualitatively assessed the image quality, artifact burden, and diagnostic interpretability of peri-implant soft and hard tissues using a 5-point discrete visual scale. Objective analyses included quantitative line profile analysis of implant-induced artifacts. Descriptive statistics were calculated, and inter-reader agreement was assessed using percentage agreement and the Krippendorff alpha coefficient.
Results
Qualitative analysis demonstrated excellent image quality for VMIs at ≥110 keV (median=5), with minimal artifacts observed at 130–150 keV. In contrast, lower-energy VMIs (70–90 keV) showed inferior performance due to artifact-related limitations in diagnostic interpretability. Inter-reader agreement ranged from moderate to perfect, with perfect reliability (α=1) for VMIs ≥110 keV. Quantitative line-profile analysis confirmed reduced artifact burden at higher energy levels, particularly for VMIs ≥110 keV.
Conclusion
VMI at energy levels ≥110 keV on PCD-CT reduced dental implant-related metal artifacts and offered excellent image quality, including assessment of both peri-implant soft and hard tissues. These findings suggest that optimized PCD-CT VMI may enhance postoperative follow-up imaging. Future in vivo studies are warranted to validate these findings in clinical practice.
Keywords: Tomography, X-Ray Computed; Dental Implants; Artifacts
Introduction
Dental implant surgery is a well-established clinical procedure with demonstrated long-term treatment efficacy for replacing missing teeth. Compared to other restorative procedures, it provides patients with superior oral function and esthetics.1 To achieve optimal outcomes in modern personalized dental implant therapy, a thorough clinical assessment of the surgical site must be complemented with indication-specific imaging. These imaging techniques are utilized at various stages of the treatment process, including preoperative planning, intraoperative guidance, and long-term monitoring of the implant site over its lifespan.2
Two-dimensional imaging is a common diagnostic tool, but is limited in accurately depicting key parameters for dental implant surgery due to geometric distortion and anatomical overlap. Consequently, 3-dimensional techniques, such as cone-beam computed tomography (CBCT), are often favored in dentomaxillofacial workflows because they provide detailed and accurate anatomical information, including bone quality, density, and regeneration around implants.3 However, challenges remain in CBCT or computed tomography (CT) imaging, primarily due to beam hardening artifacts arising from metallic implants, particularly titanium, a biocompatible material with favorable chemical and physical properties frequently used in the fabrication of dental implants.3,4 Such artifacts compromise the evaluation of peri-implant bone, osseointegration, and the detection of pathologies near implants, including tumors, inflammation, or osteolysis.5 The primary sources of peri-implant imaging artifacts are beam hardening and photon starvation. Beam hardening arises when an X-ray beam passes through dense materials or high-atomic-number elements, causing absorption of lower-energy photons and increasing the average energy of the beam.6,7 While this mechanism affects both CT and CBCT, artifacts are more pronounced in CBCT due to its typically lower tube voltage.8 Common manifestations include cupping artifacts and dark streaks or bands between high-density objects.7
CBCT has limited capability to reduce metallic artifacts, relying predominantly on optimization of scan settings and post-processing algorithms applied to 3-dimensional volume data. Scan setting optimization involves adjusting tube voltage and current, as well as detector collimation, which may increase the radiation dose.7 With conventional energy-integrating detector (EID) CT, the application of tin prefiltration to the X-ray beam–either as a standalone technique or in conjunction with reconstruction of virtual monoenergetic images (VMIs) during dual-energy scans9–has shown promising results in minimizing the impact of metallic artifacts.10 Recently, photon-counting detector CT (PCD-CT) systems, which use energy-resolving semiconductors (e.g., cadmium telluride or silicon), have been introduced.11,12 These detectors register each incident X-ray photon by converting it into an electrical pulse with an amplitude that reflects the photon’s energy, enabling spectral separation and energy discrimination. In contrast, conventional EID systems rely on an indirect 2-step process in which incoming photons are first absorbed by a scintillator material that emits visible light; this light is then captured by photodiodes and transformed into a single electrical signal, resulting in the loss of energy information from individual photons.13 Additionally, PCD-CT detectors are characterized by optimized geometric dose efficiency,14 reduced artifacts through energy weighting,15 increased contrast-to-noise ratio,11 and very high in-plane and through-plane spatial resolution.11,16
VMI is considered the routine image series on PCD-CT17 and has shown substantial potential for minimizing metalinduced artifacts18 while offering spatial resolution comparable to dental CBCT. Currently, few studies have investigated PCD-CT in dental imaging; however, initial findings have been promising, particularly for imaging dental implants and reducing metal artifacts.19,20
This ex vivo study aimed to determine the optimal energy level for VMI from PCD-CT to minimize metallic artifacts from dental implants.
Materials and Methods
Study design and ethics
The Office of Animal Welfare and 3R at the University of Zurich issued a formal statement confirming that all experimental procedures adhered to Swiss federal guidelines for the use of animals in research.
This ex vivo study utilized 6 pig mandibles provided by a local slaughterhouse in [blinded for review] to qualitatively and quantitatively assess parameters relevant to perioperative diagnostic imaging in the context of dental implant surgery. A total of 12 dental implants were placed in randomized order across both the right and left sides of the mandible, with each mandible receiving 2 implants: 1 in each quadrant between the canine and the first premolar. Surgical procedures were performed by an experienced senior physician (S.V.), a board-certified oral surgeon with over 11 years of experience. The study employed implants from 4 brands widely used in clinical practice: Dentsply Sirona (Astra Tech OsseoSpeed EV 4.2 S; Mölndal, Sweden), Nobel Biocare (NobelActive TiUltra; Göteborg, Sweden), Straumann (Standard Plus SLActive; Basel, Switzerland), and Thommen Medical (SPI®ELEMENT Implantat RC INICELL; Grenchen, Switzerland).
This study was conducted using cadaveric porcine mandible specimens, which are widely recognized as an appropriate and prevalent alternative model in orofacial research due to their anatomical resemblance to the human oral and maxillofacial system.21 All experimental procedures were reviewed by [blinded for review] and adhered to the [blinded for review] guidelines for the use of animals in research. The study reporting follows the Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines.
Image acquisition
The mandibles were imaged on a first-generation dual-source PCD-CT system (NAEOTOM Alpha; Siemens Healthineers AG, Forchheim, Germany) equipped with 2 cadmium telluride detectors. All scans were performed in spectral mode (QuantumPlus) with a detector collimation of 144×0.4 mm, a tube voltage of 140 kV, and a tube current of 30 mA. The pitch factor was 0.85. These scan settings resulted in a volume CT dose index (CTDIvol) of 7.3 mGy, a dose-length product of 184 mGy cm, and an effective dose of 368 µSv (using a conversion factor of 0.002 mSv mGy-1 cm-1).22
Scans were reconstructed as VMIs from 70 to 150 keV in 20-keV increments using the Hr60 kernel. The slice thickness and increment were 0.6 mm and 0.3 mm, respectively. The matrix size was 512×512 pixels.
Image analysis
All VMIs were assessed by 3 readers with different levels of experience and specialization: Reader A (A.A.H.), a resident in oral surgery with 4 years of experience; Reader B (N.A.H.), a senior physician board-certified in reconstructive dentistry with a Master of Advanced Studies in reconstructive and implant dentistry and 9 years of experience; and Reader C (S.V.), a senior physician board-certified in oral surgery with 11 years of experience. A calibration session was conducted prior to evaluation to standardize the assessment process. The 3 examiners received instructions from one of the study’s principal investigators. To address and eliminate potential ambiguities, 3 randomly selected cases were evaluated. All readers evaluated the scans in randomized order to ensure objective and unbiased assessments. Readers were blinded to each other’s evaluations, the VMI energy levels, and the implant types used. Quantitative analysis was performed by T.S., a resident and MD-PhD student with 5 years of experience in medical imaging research.
To evaluate general image quality and the impact of artifacts, a modified 5-point discrete visual scale adapted from Dillinger et al. for dental implant imaging with PCD-CT was applied.20 Image quality was rated as follows: 5, excellent quality with complete diagnostic assessability; 4, good quality with adequate diagnostic assessability; 3, intermediate quality with limited diagnostic assessability; 2, poor quality with minimal diagnostic assessability; and 1, very poor quality with no diagnostic capability. Ratings of 3 or higher were considered clinically diagnostic, while scores of 1 and 2 were considered non-diagnostic.
Implant-related artifacts were assessed according to the criteria of Patzer et al.23 using a modified 5-point discrete visual scale: 5, absence or near absence of metallic artifacts with no impact on quality; 4, minimal metallic artifacts that do not disrupt scan quality or diagnosis; 3, moderate metallic artifacts that affect overall quality but do not hinder assessment of nearby anatomy; 2, pronounced artifacts that impair scan quality and interfere with anatomical evaluation and diagnosis; and 1, severe artifacts that substantially compromise scan quality, obscure adjacent structures, and compromise diagnosis.
Additionally, the visualization of peri-implant soft and hard tissues for diagnostic interpretability was assessed using a 5-point discrete visual scale, as accurate delineation of these structures is crucial for planning and evaluating implant surgery: 5, fully diagnostic; 4, minor artifacts causing slight impairment; 3, artifacts resulting in mediocre interpretability; 2, artifacts with significantly impaired diagnostic interpretability; and 1, insufficient interpretability due to excessive artifacts.
To quantitatively assess artifact burden, line profiles were generated along each implant at the interface between the implant and surrounding air using the “line profile” function in the open-source software FIJI.24 Using a custom R script developed in-house, the maximum slopes of these line profiles were determined (Fig. 1). These slopes were calculated from the derivative curves of the line profiles and subsequently normalized to the maximum attenuation of the implants.11,25 This normalization accounted for variations in tissue and metal implant attenuation across different VMI levels. The resulting relative slope values served as quantitative parameters for assessing artifact burden.
Fig. 1. A. Coronal virtual monoenergetic images (VMIs) at 70 keV from photon-counting detector computed tomography showing artifact assessment by line profiles at the junction between the implant and adjacent air. B. The maximum slopes were calculated from the derivative curves of the line profiles and subsequently normalized to the maximum attenuation of the implants. This normalization accounted for variations in tissue and metal implant attenuation across VMI levels.
Importantly, lower relative slope values indicate more pronounced metal artifacts, whereas higher values indicate fewer artifacts.
Statistical analysis
All statistical analyses were performed using IBM SPSS Statistics (version 29.0.2.0; IBM Corp., Armonk, NY, USA) with a significance level of α=0.05. Descriptive statistical methods were used to evaluate the qualitative and quantitative data. Qualitative results are reported as medians with interquartile ranges (IQRs). Inter-reader agreement was evaluated and reported as either percentage agreement or the Krippendorff alpha. Interpretation of Krippendorff alpha values was as follows: 1 indicates perfect reliability, 0 indicates no reliability beyond chance, and negative values suggest systematic disagreement among readers.26
Results
Qualitative results
Three blinded readers evaluated 30 image sets, comprising 6 mandibles reconstructed at 5 VMI levels (70 keV, 90 keV, 110 keV, 130 keV, and 150 keV).
The median overall image quality was excellent, with full diagnostic assessability for all readers on VMIs at 110 keV or higher (median=5; IQRs: 5-5). At 90 keV, the median overall image quality ranged from good to excellent, with sufficient diagnostic assessability and superior performance compared to 70 keV (Table 1). All VMI levels yielded diagnostic overall image quality ratings.
Table 1. Qualitative assessment of image quality, artifacts, and peri-implant tissue visualization using PCD-CT across energy levels, rated on a 5-point scale (median [IQR]).
PCD-CT: photon-counting detector computed tomography, IQR, interquartile range, VMI: virtual monoenergetic image
Implant materials caused minimal or no artifacts on VMIs at 150 keV (median=5; IQRs: 5-5). VMIs at 130 keV and 110 keV showed comparable results, with all readers assigning a median score of 4 (all IQRs: 4-5), indicating minor metallic artifacts that did not impair diagnostic assessability or image quality. In contrast, implant-induced artifacts were notably higher at lower energy levels, particularly 90 keV and 70 keV (Table 1).
Visualization of both hard and soft tissues demonstrated excellent diagnostic assessability on VMIs at 110 keV or higher, with perfect median ratings of 5 across all 3 readers. However, artifacts compromised diagnostic interpretability at 90 keV and 70 keV (Table 1). Detailed frequency distributions of ratings are presented in Figure 2.
Fig. 2. Distribution of ordinal visual scale grading (5: most favorable, 1: least favorable) for each virtual monoenergetic image level (70–150 keV) from the qualitative assessment. A. Technical image quality. B. Implant-induced artifacts. C. Visualization of hard tissues. D. Visualization of soft tissues.
Regarding manufacturer-specific differences in susceptibility to artifacts, VMIs at 150 keV consistently exhibited minimal artifacts and superior image quality across all implant manufacturers. In comparison, artifact susceptibility increased at 90 keV and below (Table 2, Figs. 3, 4, 5).
Table 2. Comparison of implant-specific artifacts and image quality (median [IQR]).
PCD-CT: photon-counting detector computed tomography, VMI: virtual monoenergetic image, IQR: interquartile range
Fig. 3. Coronal (A and D), axial (B and E), and sagittal (C and F) virtual monoenergetic images from a photon-counting detector computed tomography scan at energy levels of 70 keV and 90 keV show a dental implant placed between the canine and first premolar in a pig mandible. The images illustrate implant-induced artifacts, including streaks and beam hardening, which were more pronounced at lower energy levels.
Fig. 4. Coronal, axial, and sagittal virtual monoenergetic images (VMIs) from a photon-counting detector computed tomography scan at energy levels of 110 keV (A–C), 130 keV (D–F), and 150 keV (G–I) depict minimal to no implant-induced metallic artifacts between the canine and first premolar in a pig mandible. The images demonstrate decreasing artifact susceptibility as VMI energy increases.
Fig. 5. A. Coronal cinematic rendering image. B–F. Virtual monoenergetic images (VMIs) at varying energy levels (70–150 keV) from a photon-counting detector computed tomography scan, demonstrating visualization of both hard and soft tissues. VMIs at 110 keV or higher consistently achieved perfect scores in visual grading, whereas lower-energy VMIs reduced visualization of both soft and hard tissues.
Inter-reader agreement, as indicated by percentage agreement, ranged from moderate (66.7%) to perfect (100%) across all evaluated parameters and reconstructions among the 3 readers (Table 3). Protocol-specific agreement, assessed with the Krippendorff alpha, showed excellent reliability for VMIs of 110 keV and higher (all α=1), with lower agreement at 90 keV and 70 keV (Table 4).
Table 3. Inter-reader agreement for technical image quality, artifacts, and peri-implant tissue imaging, expressed as percentage agreement among 3 readers.
PCD-CT: photon-counting detector computed tomography, VMI: virtual monoenergetic image
Table 4. Inter-reader agreement as measured by the Krippendorff alpha26 for each VMI level.
VMI: virtual monoenergetic image, PCD-CT: photon-counting detector computed tomography
Quantitative results
Across all manufacturers, higher relative slope values–indicating lower implant-induced artifact burden–were observed on VMIs at higher energy levels. This trend was particularly evident at 110 to 150 keV, with median values ranging from 1.78 to 1.91/mm (Table 5).
Table 5. Artifact burden of dental implants across VMI levels (median [IQR]).
VMI: virtual monoenergetic image, PCD-CT: photon-counting detector computed tomography, IQR: interquartile range
In the manufacturer-specific analysis, the same pattern was observed, with Nobel Biocare (NobelActive TiUltra) exhibiting the lowest artifact burden on VMIs at 150 keV (median 2.04/mm; Table 6).
Table 6. Quantitative artifact burden by manufacturer (median [IQR]).
PCD-CT: photon-counting detector computed tomography, VMI: virtual monoenergetic image, IQR: interquartile range
Discussion
This ex vivo study quantitatively and qualitatively evaluated VMI levels from PCD-CT to identify the optimal VMI energy level that minimizes implant-induced artifacts in imaging for dental implant surgery. The results indicate that VMIs at higher energies (≥110 keV) provide slight but consistent advantages in reducing metal artifacts compared with lower energies, even though all tested levels achieved clinically diagnostic image quality. This approach may be particularly beneficial in dental and cranio-maxillofacial imaging, where artifacts frequently pose a postoperative challenge.
The current use of conventional EID-CT and CBCT in perioperative oral and maxillofacial surgery can produce hypodense and hyperdense artifacts in reconstructed images because of scanner geometry, X-ray beam energy, and backprojection–based reconstruction.7,27 Metallic structures, such as dental implants, further exacerbate these artifacts, leading to diagnostic issues–including noise, beam hardening, streak artifacts, and photon starvation7–that can obscure critical information required for accurate diagnosis.
The spectral capabilities of PCD-CT enable reconstruction of VMIs at multiple energy levels. These advances have been shown to improve the depiction of dental structures and significantly reduce artifacts caused by dental implants.28,29 Layer et al.30 reported significant enhancements in image quality when using VMIs from 100 to 190 keV compared with standard imaging protocols. In that study, VMI at 130 keV achieved the greatest reduction in dental implant-related artifacts in PCD-CT scans of the head and neck and enabled superior visualization of adjacent soft tissues, such as the soft palate, thereby improving diagnostic accuracy for parameters critical for perioperative dental implant surgery. In the present study, excellent diagnostic performance was observed with VMIs at ≥110 keV, further supporting the use of higher-energy VMIs to mitigate beam hardening and scattering effects associated with metallic dental implants across multiple manufacturers. Conversely, lower-energy VMIs–particularly 70 keV–showed impaired image quality and a pronounced artifact burden, limiting their clinical utility. These trends were also evident in peri-implant imaging of hard and soft tissues. The findings were confirmed by both qualitative and quantitative assessments, underscoring the importance of tailoring VMI settings to the diagnostic requirements of peri-implant imaging. Additionally, perfect inter-reader agreement was observed for VMIs at or above 110 keV, independent of reader experience or specialization.
The evaluation included implants from several manufacturers. Only minimal variation in artifact susceptibility was observed across manufacturers at higher-energy VMIs, highlighting the robustness of this approach in delivering consistent diagnostic performance regardless of implant material or design. This uniformity is particularly valuable in clinical practice, where diverse implant types are common. At lower-energy VMIs, however, manufacturer-specific differences became more pronounced, likely reflecting variations in implant geometry and composition.
The capacity of PCD-CT to depict surgery–related periimplant parameters-including precise assessment of soft and hard tissues–is crucial for comprehensive preoperative planning, accurate intraoperative guidance, and effective postoperative monitoring. The findings of this study indicate that PCD-CT, particularly with optimized VMI settings, has the potential to overcome limitations inherent to conventional CBCT imaging, including diminished artifact suppression and spatial resolution.4,31 The high inter-reader agreement observed here underscores the reliability of PCD-CT for consistent diagnostic interpretation by clinicians with varying levels of experience and specialization, which is essential for effective patient care.
Several conventional strategies have been employed to reduce metal artifacts in dental implant imaging, including adjustments to CBCT acquisition parameters, dual-energy CT-based VMI reconstructions, and iterative metal artifact reduction (iMAR) algorithms.32,33,34 While these approaches can improve image quality, they are often limited by increased noise, higher radiation dose, or reduced effectiveness in the immediate vicinity of metallic implants compared with PCD-CT.13 In contrast, PCD-CT inherently resolves photon energies, enabling routine VMI reconstruction that consistently minimizes metal-induced artifacts without requiring complex post-processing. Accordingly, this study focused exclusively on evaluating specific VMI levels, without investigating advanced reconstruction algorithms for metal artifact reduction or including comparisons to conventional polychromatic imaging. This design promoted methodological consistency and enabled isolated, reproducible analysis of VMI effects. Reports in the literature indicate that artifacts adjacent to metallic implants, such as blurring, can arise when using iMAR algorithms; these may mimic pathologies and lead to false diagnoses.35,36 Such artifacts may result from data loss at the metal edge during interpolation.37 Although iMAR, either alone or combined with VMI, generally improves image quality in regions distant from implants,23,38,39,40 its reliability is limited in the immediate peri-implant region.36 In contrast, higher-energy VMIs are not subject to these limitations.
The present study has several limitations. First, comparative analyses with other imaging technologies, such as CBCT, were not performed. Second, the number of implants from different vendors was limited, allowing only descriptive comparisons. Third, the ex vivo design does not fully replicate in vivo human conditions, including variations in tissue composition and the effects of patient movement. Further research is essential to validate these findings in clinical settings with larger cohorts and to evaluate the applicability of PCD-CT in dentomaxillofacial imaging. Fourth, scans were conducted using only 1 radiation dose, and potential dose-reduction benefits of PCD-CT compared with conventional CT protocols–an important consideration for clinical applications and patient safety–were not assessed. Future studies should quantify and compare radiation exposure to provide a more comprehensive evaluation of the dose efficiency of PCD-CT in clinical settings. Fifth, although both subjective and objective image analyses showed high consistency, no formal statistical hypothesis testing was performed. Additionally, the readers were experienced dentists rather than specialized radiologists, which may have limited their sensitivity to subtle imaging artifacts. Future studies should include a broader range of readers and apply formal statistical testing to further validate these findings.
In conclusion, higher-energy VMIs on PCD-CT, particularly at 110 keV and above, demonstrated superior qualitative and quantitative diagnostic performance for implant imaging compared with lower-energy VMIs. By effectively reducing dental implant-related metallic artifacts–regardless of implant type–and with strong inter-reader agreement, this approach has the potential to improve perioperative image quality and diagnostic interpretability for both hard and soft tissue assessments in dental implant surgery.
Footnotes
Conflicts of Interest: None
References
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