High-quality image acquisition by double exposure overlap in dental cone beam computed tomography

Radiographic alveolar bone assessment in correlation with primary implant stability: A systematic review and meta-analysis

INTRODUCTION

The radiographic examination of alveolar bone using 3D radiographic examination is essential in dental implant treatment planning. Our study aimed to systematically review and quantitatively analyze the correlation between alveolar bone parameters, specifically bone density and cortical bone thickness, assessed using cone beam computed tomography (CBCT) and/or multidetector computed tomography (MDCT); and primary implant stability (PIS) determined using implant stability quotient (ISQ), Periotest® value (PTV), and insertion torque value (ITV).

METHODS

This review was registered in the PROSPERO database (registration number CRD42022307245). An electronic literature search was conducted on the PubMed, SCOPUS, and Web of Science databases for papers published until February 2022. The Quality Assessment in Prognostic Studies (QUIPS) tool was used to assess risk of bias. Meta-analyses were conducted to calculate the estimated average correlation coefficient based on a multilevel random-effects model, followed by subgroup analysis.

RESULTS

Twenty-six studies were included in this review, consisting of 17 prospective cohort studies, eight retrospective cohort studies, and one nonrandomized controlled trial. A total of 3109 implants placed in 1171 subjects were analyzed. Twenty-three studies were evaluated using meta-analysis. The alveolar bone condition was significantly correlated with ISQ (r = 0.60; p < .001), IT (r = 0.52; p < .001), and PTV (r = -0.42; p < .05).

CONCLUSION

Alveolar bone condition is significantly associated with PIS. Low bone density and thin cortical bone can lead to low PIS; therefore, modification of treatment planning and surgical procedures might be needed to avoid poor osseointegration.

Validation of bone mineral density measurement using quantitative CBCT image based on deep learning

The bone mineral density (BMD) measurement is a direct method of estimating human bone mass for diagnosing osteoporosis, and performed to objectively evaluate bone quality before implant surgery in dental clinics. The objective of this study was to validate the accuracy and reliability of BMD measurements made using quantitative cone-beam CT (CBCT) image based on deep learning by applying the method to clinical data from actual patients. Datasets containing 7500 pairs of CT and CBCT axial slice images from 30 patients were used to train a previously developed deep-learning model (QCBCT-NET). We selected 36 volumes of interest in the CBCT images for each patient in the bone regions of potential implants sites on the maxilla and mandible. We compared the BMDs shown in the quantitative CBCT (QCBCT) images with those in the conventional CBCT (CAL_CBCT) images at the various bone sites of interest across the entire field of view (FOV) using the performance metrics of the MAE, RMSE, MAPE (mean absolute percentage error), R² (coefficient of determination), and SEE (standard error of estimation). Compared with the ground truth (QCT) images, the accuracy of the BMD measurements from the QCBCT images showed an RMSE of 83.41 mg/cm³, MAE of 67.94 mg/cm³, and MAPE of 8.32% across all the bone sites of interest, whereas for the CAL_CBCT images, those values were 491.15 mg/cm³, 460.52 mg/cm³, and 54.29%, respectively. The linear regression between the QCBCT and QCT images showed a slope of 1.00 and a R² of 0.85, whereas for the CAL_CBCT images, those values were 0.32 and 0.24, respectively. The overall SEE between the QCBCT images and QCT images was 81.06 mg/cm³, whereas the SEE for the CAL_CBCT images was 109.32 mg/cm³. The QCBCT images thus showed better accuracy, linearity, and uniformity than the CAL_CBCT images across the entire FOV. The BMD measurements from the quantitative CBCT images showed high accuracy, linearity, and uniformity regardless of the relative geometric positions of the bone in the potential implant site. When applied to actual patient CBCT images, the CBCT-based quantitative BMD measurement based on deep learning demonstrated high accuracy and reliability across the entire FOV.

Comparison of the Hounsfield unit in CT scan with the gray level in cone-beam CT

Background. The present study was undertaken to compare the Hounsfield Unit (HU) in computed tomography (CT) with the gray level in CBCT in human tissues.

Methods. In this study, 25 different soft and hard tissues were evaluated in 21 patients. CBCT images were taken with Newtom VGi machine (Verona, Italy) and CT images were prepared with Somatom Sensation unit (Siemens, Germany). The HU values of soft and hard tissues were compared with the gray level values of CBCT images.

Results. There was a strong correlation between the HU in CT and the gray level in CBCT in soft tissues (P<0.001, R² = 0.85) and hard tissues (P<0.001, R² = 0.74) and in general (P<0.001, R² = 0.91).

Conclusion. A high degree of agreement was seen between HU in CT and gray level in CBCT in both hard and soft tissues. Since the gray level in CBCT was similar to HU in CT and can be used as a parameter determine bone density in implant treatment and also to determine the bone type, the CBCT technique is recommended in such cases due to its low radiation dose, short time and low cost compared to CT.

Change in Image Quality According to the 3D Locations of a CBCT Phantom

A patient's position changes in every CBCT scan despite patient alignment protocols. However, there have been studies to determine image quality differences when an object is located at the center of the field of view (FOV). To evaluate changes in the image quality of the CBCT scan according to different object positions, the image quality indexes of the Alphard 3030 (Alphard Roentgen Ind., Ltd., Kyoto, Japan) and the Rayscan Symphony (RAY Ind., Ltd., Suwon, Korea) were measured using the Quart DVT_AP phantom at the center of the FOV and 6 peripheral positions under four types of exposure conditions. Anterior, posterior, right, left, upper, and lower positions 1 cm offset from the center of the FOV were used for the peripheral positions. We evaluated and compared the voxel size, homogeneity, contrast to noise ratio (CNR), and the 10% point of the modulation transfer function (MTF10%) of the center and periphery. Because the voxel size, which is determined by the Nyquist frequency, was within tolerance, other image quality indexes were not influenced by the voxel size. For the CNR, homogeneity, and MTF10%, there were peripheral positions which showed considerable differences with statistical significance. The average difference between the center and periphery was up to 31.27% (CNR), 70.49% (homogeneity), and 13.64% (MTF10%). Homogeneity was under tolerance at some of the peripheral locations. Because the CNR, homogeneity, and MTF10% were significantly affected by positional changes of the phantom, an object's position can influence the interpretation of follow up CBCT images. Therefore, efforts to locate the object in the same position are important.

CBCT-based bone quality assessment: are Hounsfield units applicable?

CBCT is a widely applied imaging modality in dentistry. It enables the visualization of high-contrast structures of the oral region (bone, teeth, air cavities) at a high resolution. CBCT is now commonly used for the assessment of bone quality, primarily for pre-operative implant planning. Traditionally, bone quality parameters and classifications were primarily based on bone density, which could be estimated through the use of Hounsfield units derived from multidetector CT (MDCT) data sets. However, there are crucial differences between MDCT and CBCT, which complicates the use of quantitative gray values (GVs) for the latter. From experimental as well as clinical research, it can be seen that great variability of GVs can exist on CBCT images owing to various reasons that are inherently associated with this technique (i.e. the limited field size, relatively high amount of scattered radiation and limitations of currently applied reconstruction algorithms). Although attempts have been made to correct for GV variability, it can be postulated that the quantitative use of GVs in CBCT should be generally avoided at this time. In addition, recent research and clinical findings have shifted the paradigm of bone quality from a density-based analysis to a structural evaluation of the bone. The ever-improving image quality of CBCT allows it to display trabecular bone patterns, indicating that it may be possible to apply structural analysis methods that are commonly used in micro-CT and histology.

The effect of calibration and detector temperature on the reconstructed cone beam computed tomography image quality: a study for the workflow of the iCAT Classic equipment

OBJECTIVE

The image quality of the reconstructed dental cone beam computed tomography (CBCT) acquisition is strongly dependent on the characteristics of the applied detector. Some flat panel detector (FPD) types require a warming-up period (WUP) to achieve a steady-state temperature; and some of them, those that have a cesium iodide detector, may require a calibration process (CP) after each WUP.

STUDY DESIGN

Quality-assurance phantom was used for the evaluation of image quality, including spatial accuracy and density response with and without WUP and CP using iCAT Classic equipment.

RESULTS

The correlation between the measured gray values and the multidetector computed tomography (MDCT) values deviated slightly from linearity (defined by the 45-degree line). There was no detectable difference in the spatial accuracy of the four different scanning modes.

CONCLUSIONS

Although the WUP is important to reach the required steady-state temperature, the CP has a greater effect on the image quality.