Cone-beam Computed Tomography Volume Research Articles

Influence of Cone-beam CT Volume Orientation on the Proximity of Maxillary Sinus to the Alveolar Crest at Dental Implant Sites

Influence of Cone-beam CT Volume Orientation on the Proximity of Maxillary Sinus to the Alveolar Crest at Dental Implant Sites

Prevalence of periapical lesions, root canal treatments and restorations in teeth adjacent to implant- or tooth-supported crowns: A multi-centre cross-sectional study.

To describe the prevalence of periapical lesions, root canal treatments and coronal restorations on teeth adjacent to either implant- or natural tooth-supported crowns using cone-beam computed tomography (CBCT) assessment compared to nonadjacent teeth. A total of 1249 CBCT volumes were screened by five pre-calibrated observers in 11 health centres. A step-by-step screening protocol was implemented and reviewed every 3months, and mandatory setting characteristics were established for all CBCT scans using dedicated visualization software. Intra- and inter-rater reliability tests were conducted. The prevalence of periapical lesions, root canal treatments and coronal restorations was recorded on both teeth adjacent and nonadjacent to implant- (predictor 1) or natural tooth-supported crowns (predictor 2). A binary logistic model (Generalizing Estimating Equations test) was used to verify whether the prevalence of periapical lesion, root canal filling and the tooth restorative status are altered when the assessed tooth is adjacent or not to an implant-supported crown (predictor 1); or to a natural tooth-supported crown (predictor 2). Odds ratio and confidence intervals for the dependent variables at both predictors were obtained. The significance level was set at .05. A global sample of 22899 teeth was included. Compared to nonadjacent teeth, the prevalence of periapical lesion, root canals treatments and restorative procedures when adjacent to implant-supported crowns was 10.7%, 19.6% and 22.9% higher, respectively, and when adjacent to tooth-supported crowns was 19.3%, 35.6% and 37.4% higher respectively. These results were significant only for variables root canal filling and coronal restoration (p<.0001). Odds ratio to present root canal treatment is 2.57 times higher (CI 1.95-3.39, p=.0001) when the tooth is adjacent to implant-supported crown and 4.39 times (CI 3.49-5.53, p=.0001) when adjacent to tooth-supported crown, whilst for restorative procedure, the odds are, respectively, 1.63 (CI 1.29-2.06, p=.0001) and 2.30 (CI 1.92-2.76, p=.0001). Teeth adjacent to both implant- and natural tooth-supported crowns were associated with a higher frequency of root canal filling and coronal restorations.

Image-based shading correction for narrow-FOV truncated pelvic CBCT with deep convolutional neural networks and transfer learning.

Purpose: Cone beam computed tomography (CBCT) is a standard solution for in‐room image guidance for radiation therapy. It is used to evaluate and compensate for anatomopathological changes between the dose delivery plan and the fraction delivery day. CBCT is a fast and versatile solution, but it suffers from drawbacks like low contrast and requires proper calibration to derive density values. Although these limitations are even more prominent with in‐room customized CBCT systems, strategies based on deep learning have shown potential in improving image quality. As such, this article presents a method based on a convolutional neural network and a novel two‐step supervised training based on the transfer learning paradigm for shading correction in CBCT volumes with narrow field of view (FOV) acquired with an ad hoc in‐room system. Methods: We designed a U‐Net convolutional neural network, trained on axial slices of corresponding CT/CBCT couples. To improve the generalization capability of the network, we exploited two‐stage learning using two distinct data sets. At first, the network weights were trained using synthetic CBCT scans generated from a public data set, and then only the deepest layers of the network were trained again with real‐world clinical data to fine‐tune the weights. Synthetic data were generated according to real data acquisition parameters. The network takes a single grayscale volume as input and outputs the same volume with corrected shading and improved HU values. Results: Evaluation was carried out with a leave‐one‐out cross‐validation, computed on 18 unique CT/CBCT pairs from six different patients from a real‐world dataset. Comparing original CBCT to CT and improved CBCT to CT, we obtained an average improvement of 6 dB on peak signal‐to‐noise ratio (PSNR), +2% on structural similarity index measure (SSIM). The median interquartile range (IQR) Hounsfield unit (HU) difference between CBCT and CT improved from 161.37 (162.54) HU to 49.41 (66.70) HU. Region of interest (ROI)‐based HU difference was narrowed by 75% in the spongy bone (femoral head), 89% in the bladder, 85% for fat, and 83% for muscle. The improvement in contrast‐to‐noise ratio for these ROIs was about 67%. Conclusions: We demonstrated that shading correction obtaining CT‐compatible data from narrow‐FOV CBCTs acquired with a customized in‐room system is possible. Moreover, the transfer learning approach proved particularly beneficial for such a shading correction approach.

Evaluation of Automated Breast Cancer Contour Propagation From Planning CT to Cone Beam CT Based on Deep Learning

<h3>Purpose/Objective(s)</h3> To evaluate a regional deformable model based on deep unsupervised learning framework for automated contour propagation in breast Cone Beam Computed Tomography (CBCT) guided radiation therapy. <h3>Materials/Methods</h3> We introduce an unsupervised learning framework to map the breast tumor bed, clinical target volume (CTV), heart, left lung, right lung, and spinal cord from planning CT (pCT) to daily CBCT. To improve the performance of the traditional deformable image registration method, the proposed unsupervised learning method uses a regional deformable model based on the narrow band mapping, which can mitigate the effect of the image artifacts. We retrospectively selected 380 anonymized CBCT volumes from 107 patients with breast cancer. The CBCTs are divided into three sets. 311 / 20 / 49 CBCTs were used for training, validation, and testing. For the testing set, one physician-generated contours were generated as the references. Through the Dice similarity coefficients (DSC), the Hausdorff distances, and the distances of the center-of-mass, the results were compared between the proposed model and physician-generated contours. <h3>Results</h3> The mean DSC between the proposed segmentations and the physician-generated segmentations for breast tumor bed, clinical target volume (CTV), heart, left lung, right lung, and spinal cord were 0.79 ± 0.09, 0.89 ± 0.08, 0.90 ± 0.08, 0.93 ± 0.03, 0.94 ± 0.04, and 0.76 ± 0.13, respectively. The average distances of the center-of-mass for the whole testing images were less than 4 mm. The proposed regional deformable model's performance was better than the traditional global deformable model, especially in the CBCT image with severe image artifacts. <h3>Conclusion</h3> This novel deep learning-based regional deformable model technique can automatically propagate contours for breast cancer patient radiotherapy. The proposed approach shows that the deep learning technique can accomplish highly precise contour propagation for breast CBCT-guided adaptive radiotherapy.

Automated Contour Propagation of the Prostate From pCT to CBCT Images via Deep Unsupervised Learning

To develop and evaluate a deep unsupervised learning (DUL) framework based on a regional deformable model for automated prostate contour propagation from planning computed tomography (pCT) to cone-beam CT (CBCT).A DUL model was introduced to map the prostate contour from pCT to on-treatment CBCT. The DUL framework used a regional deformable model via narrow band mapping to augment the conventional strategy. 251 anonymized CBCT images from prostate cancer patients were retrospectively selected and divided into three sets: 180 were used for training, 12 for validation, and 59 for testing. The testing dataset was divided into two Groups. Group one contained 50 CBCT volumes, with one physician-generated prostate contour on CBCT image. Group two contained 9 CBCT images, each including prostate contours delineated by four independent physicians and a consensus contour generated using the STAPLE method. Results were compared between the proposed DUL and physician-generated contours through the Dice similarity coefficients (DSC), the Hausdorff distances, and the distances of the center-of-mass.The average DSCs between DUL-based prostate contours and reference contours for test data in Group one and Group two-consensus were 0.83 ± 0.04, and 0.85 ± 0.04, respectively. Correspondingly, the mean center-of-mass distances were 3.52 mm ± 1.15 mm, and 2.98 mm ± 1.42 mm, respectively.This novel DUL technique can automatically propagate the contour of the prostate from pCT to CBCT. The proposed method shows that highly accurate contour propagation for CBCT-guided adaptive radiotherapy is achievable via the deep learning technique.

Image quality assessment of low-dose protocols in cone beam computed tomography of the anterior maxilla

To evaluate overall image quality and visibility of anatomic structures on low-dose cone beam computed tomography (CBCT) scans and the effect of a noise reduction filter for assessment of the anterior maxilla. We obtained 48 CBCT volumes on 8 skull-phantoms using 6 protocols: 2 clinical default protocols [standard definition (SD) and high definition (HD)] and 4 low-dose protocols, 2 with a noise reduction filter [ultra-low-dose with high definition (ULDHD) and ultra-low-dose (ULD)] and 2 without [low-dose with high definition (LDHD) and low-dose (LD)]. Overall image quality and visibility of 8 anatomic structures were assessed by 5 observers and statistically analyzed using the Wilcoxon signed rank test. Intra- and interobserver agreement was measured using Cohen's weighted kappa. HD provided higher overall image quality than diagnostically required; LD scored lower than diagnostically acceptable. ULDHD, ULD, and LDHD were acceptable. For anatomic structures, ULDHD and ULD were acceptable. LDHD and LD showed significantly inferior visibility for 1 and 4 structures, respectively. Mean values of intra- and interobserver agreement were 0.395 to 0.547 and 0.350 to 0.370, respectively. ULDHD, ULD, and LDHD may be recommended for assessment of impacted maxillary canines. The noise reduction filter affects image quality positively only at low exposure.

Intelligent quantitative assessment of skeletal maturation based on multi-stage model: a retrospective cone-beam CT study of cervical vertebrae.

To develop new logistic regression estimative models of the cervical vertebral maturation index (CVMI) based on cone-beam CT (CBCT)-derived parameters for intelligent evaluating skeletal maturation. From 231 CBCT volumes (age range 7-17, mean age 11.09years), 154 were randomly selected to produce 2D sagittal projections of the second to fourth cervical vertebrae (C2-C4). From 19 quantitative parameters, significant predictors were deduced to formulate logistic models. Using the CVMI and significant predictors of 77 other subjects, performance of the models was externally examined by direct comparison and the area under the receiver operating characteristic curve (AUC). Models were modified if required, to improve their accuracy. Chronological age, C3 height ([Formula: see text], and ratio of posterior height to lower width of C4 [Formula: see text] were entered as significant predictors. Accuracy of the models was acceptable (total AUC = 0.91) except for 4th and 5th stage (AUC of 0.82 and 0.83, respectively), which were mis-predicted inversely. Adjusted models were generated by bivariate logistic regression analysis and adding significant parameters ([Formula: see text] and [Formula: see text], with odds ratios of 3.308 and 3.38, respectively) from 58 subjects in 4th and 5th stages of CVMI in the model establishment group. The total AUC increased to 0.94, along with an increase in the accuracy of the latter optimized models to 77.9 and 87%, respectively. The new intelligent models reliably estimated skeletal maturation and can be utilized in the clinical field or machine learning-based skeletal maturation assessment.

Effect of Amalgam Restorations and Operation Parameters on Diagnostic Accuracy of Caries Detection Using Cone Beam Computed Tomography: An In Vitro Study.

This study was aimed to evaluate the diagnostic accuracy of cone beam computed tomography (CBCT) in detecting noncavitated approximal caries at different exposure parameters and to assess the impact of artifacts generated by amalgam restorations in an in vitro study. Seventy-eight approximal surfaces of extracted teeth were prepared with intentionally created noncavitated approximal caries of different depths; then, thirteen teeth with class 2 amalgam restorations were replaced with one tooth with normal surfaces in each block. CBCT volumes for all teeth were acquired using a Planmeca Promax 3D Mid imaging unit before and after placement of amalgam teeth, with different exposure parameters at low and high definition, both applying and omitting the Metal Artifact Reduction algorithm. The lesions were classified into four groups with regard to lesion extension. All teeth underwent histological analysis as gold standard. The histological examination showed that the distribution of lesions was as follows: 39.8% sound, enamel lesions of less and more than half the enamel thickness each 17.8%, and 24.6% dentin lesions. The detection sensitivity was found to be 0.972%, and specificity was found to be 0.937% for the detection of noncavitated approximal initial enamel and dentin caries. The highest diagnostic accuracy was found when using operating parameters of 90 kVp, 8 mA, and high resolution (75 μm) with nonamalgam teeth; all modes showed statistically significant higher AUCs than mode 2 (80 kVp, 7 mA, and 75 μm). However, for teeth with amalgam restorations, the highest accuracy was obtained at low resolution (200 μm) with the other parameters kept the same. It could be concluded that increasing the peak voltage and current improves diagnostic accuracy for the detection of noncavitated approximal caries. Moreover, diagnostic accuracy was found to be higher upon using high spatial resolution when diagnosing caries without adjacent amalgam restorations. There is a statistically significant difference with and without amalgam with respect to all modes.

IMPACT OF THE COMBINED EFFECT OF EXOMASS-RELATED AND MOTION ARTIFACTS IN CONE BEAM COMPUTED TOMOGRAPHY

BACKGROUNDExomass-related and motion artifacts are commonly observed in the everyday clinic and negatively affect cone beam computed tomography (CBCT) image quality. These 2 entities can be expected to occur in conjunction; however, they have only been assessed separately by the scientific community.OBJECTIVE(S)To assess quantitatively the combined effect of exomass-related and motion artifacts on voxel value parameters in CBCT.STUDY DESIGNA cylindrical phantom was manufactured containing 21 tubes filled with a radiopaque solution, allowing the inclusion of 3 titanium implants in the periphery to induce exomass-related artifacts. The phantom was mounted on a robot simulating 0.75-, 1.5-, and 3-mm movements (nodding/lateral rotation/tremor). CBCT images with/without exomass and with/without movements were acquired in duplicate in 3 units: Cranex 3Dx, Orthophos SL-3D, and X1 (with motion artefact correction). A cylindrical volume of interest was defined in each tube and voxel value means and standard deviations were assessed. For each CBCT volume, the 21 mean voxel values were averaged, providing the overall mean voxel value (MVV). The standard deviation (among the 21 values) was calculated, providing overall voxel value inhomogeneity (VVI). The standard deviations from each of the 21 volumes of interest were averaged, providing overall image noise. The effect of the diverse tested situations was inferred from a repeated-measures analysis of variance, followed by Sidak's test (α = .05).RESULTSOverall, images acquired with exomass showed significantly lower MVV and higher VVI and image noise (P ≤ .05). Motion artifacts aggravated exomass-related alterations. MVV and VVI were mostly affected by 3-mm nodding movements. Motion artifact correction eliminated the deleterious effect of movement.DISCUSSION/CONCLUSIONSCBCT voxel value parameters are altered by exomass-related artifacts, and this finding is aggravated in the presence of motion artifacts. Motion artifact correction effectively eliminated the deleterious effect of movement. Exomass-related and motion artifacts are commonly observed in the everyday clinic and negatively affect cone beam computed tomography (CBCT) image quality. These 2 entities can be expected to occur in conjunction; however, they have only been assessed separately by the scientific community. To assess quantitatively the combined effect of exomass-related and motion artifacts on voxel value parameters in CBCT. A cylindrical phantom was manufactured containing 21 tubes filled with a radiopaque solution, allowing the inclusion of 3 titanium implants in the periphery to induce exomass-related artifacts. The phantom was mounted on a robot simulating 0.75-, 1.5-, and 3-mm movements (nodding/lateral rotation/tremor). CBCT images with/without exomass and with/without movements were acquired in duplicate in 3 units: Cranex 3Dx, Orthophos SL-3D, and X1 (with motion artefact correction). A cylindrical volume of interest was defined in each tube and voxel value means and standard deviations were assessed. For each CBCT volume, the 21 mean voxel values were averaged, providing the overall mean voxel value (MVV). The standard deviation (among the 21 values) was calculated, providing overall voxel value inhomogeneity (VVI). The standard deviations from each of the 21 volumes of interest were averaged, providing overall image noise. The effect of the diverse tested situations was inferred from a repeated-measures analysis of variance, followed by Sidak's test (α = .05). Overall, images acquired with exomass showed significantly lower MVV and higher VVI and image noise (P ≤ .05). Motion artifacts aggravated exomass-related alterations. MVV and VVI were mostly affected by 3-mm nodding movements. Motion artifact correction eliminated the deleterious effect of movement. CBCT voxel value parameters are altered by exomass-related artifacts, and this finding is aggravated in the presence of motion artifacts. Motion artifact correction effectively eliminated the deleterious effect of movement.

QS4: A Quantitative Analysis Of The Upper Airway Volume In Patients With Attention Deficit Hyperactivity Disorder: A Cone-Beam Computed Tomography Study

Purpose:Attention deficit hyperactivity disorder (ADHD) is a developmental disorder affecting the quality of life. The etiology of ADHD is unclear, yet it has been believed to be multifactorial. he aim of this Cone-beam computed tomography (CBCT) study was to assess the upper airway morphology in adolescents with ADHD compared to controls.Methods:A total of 454 patients who had CBCT taken as part of their initial orthodontic records were reviewed for eligibility. 87 subjects were included in the study. CBCTs volumes were utilized for three-dimensional evaluation of volumetric, minimum cross-section area and linear measurements of the upper airway in different planes. Two sample T-test and Man-Whitney U test were used to calculate and compare the mean values of the airway measurements between ADHD and control groups.Results:There were significant differences in airway widths at planes A (p =0.002), C (p =0.042) and D (p <0.001), and in airway area at plane D (lower hypopharynx) (p <0.001), with the ADHD group showing narrower widths and area compared to controls. The mean overall airway volume in the ADHA group was smaller than in the control group but the difference was not significant.Conclusion:ADHD-affected adolescents have narrower upper airway dimensions compared to controls. Further investigations with larger samples are warranted to further elucidate the relationship between sleep-disordered breathing and ADHD.

Where is the center of resistance of a maxillary first molar? A 3-dimensional finite element analysis

<h3>Introduction</h3> The center of resistance (C_Res) is regarded as the fundamental reference point for predictable tooth movement. Accurate estimation can greatly enhance the efficiency of orthodontic tooth movement. Only a handful of studies have evaluated the C_Res of a maxillary first molar; however, most had a low sample size (in single digits), used idealized models, or involved 2-dimensional analysis. The objectives of this study were to: (1) determine the 3-dimensional (3D) location of the C_Res of maxillary first molars, (2) evaluate its variability in a large sample, and (3) investigate the effects of applying orthodontic load from 2 directions on the location of the C_Res. <h3>Methods</h3> Cone-beam computed tomography scans of 50 maxillary molars from 25 patients (mean age, 20.8 ± 8.7 years) were used. The cone-beam computed tomography volume images were manipulated to extract 3D biological structures via segmentation. The segmented structures were cleaned and converted into virtual mesh models made of tetrahedral triangles having a maximum edge length of 1 mm. The block, which included the molars and periodontal ligament, consisted of a mean of 7753 ± 2748 nodes and 38,355 ± 14,910 tetrahedral elements. Specialized software was used to preprocess the models to create an assembly and assign material properties, interaction conditions, boundary conditions, and load applications. Specific loads were applied, and custom-designed algorithms were used to analyze the stress and strain to locate the C_Res. The C_Res was measured in relation to the geometric center of the buccal surface of the molar and the trifurcation of the molar roots. <h3>Results</h3> The average location of the C_Res for the maxillary first molar was 4.94 ± 1.39 mm lingual, 2.54 ± 2.7 mm distal, and 7.86 ± 1.66 mm gingival relative to the geometric center of the buccal surface of the molar and 0.136 ± 1.51 mm lingual (<i>P</i> <0.01), 1.48 ± 2.26 mm distal (<i>P</i> <0.01), and 0.188 ± 1.75 mm gingival (<i>P</i> >0.01) relative to the trifurcation of the molar roots. In the anteroposterior (y-axis) and the vertical (z-axis) planes, the C_Res showed significant association with root divergence (<i>P</i> <0.01). <h3>Conclusions</h3> The C_Res of the maxillary first molar was located apical and distal to the trifurcation area. It showed significant variation in its location. The 3D location of and also varied with the force direction. In some samples, this deviation was large. For accurate and predictable movement, tooth-specific C_Res need to be calculated.

Long-term stability of basilar mandible osteotomy: Chin Wing

The Chin Wing is a modified genioplasty extended along the basilar border and the angles of the mandible. This technique may be a better choice than standard genioplasty for correction of lip incompetence, retrogenia and high angle position associated with hyperdivergent cases. Our objective was to analyse the overall movement realized during surgery and the long-term stability of this procedure. Ten patients underwent a Chin Wing surgery from June 2018 to August 2019. All patients were operated on by the same surgeon. We performed a preoperative (PO), an immediate postoperative (POI), and an over 6 months postoperative (PO6) Cone Beam Computed Tomography (CBCT) for every patient. 3D reconstructions were performed for each CBCT with Proplan software. We were thus able to determine by subtractions acquired and resorbed bone volume. Some section plans were chosen in order to perform 2D measurements. The CBCT volume comparisons reveal a gain of 7.6cc between the PO and the POI, for a bone resorption of 2.5cc between POI and PO6 (33% of the volume gained). In 2D evaluation, we observe an average resorption of 1.7 mm corresponding to approximately 20% of the height gained, at the level of the mandibular angle. Given the low bone resorption we can consider Chin Wing stable over time. The important quantity of mobilized bone and the basilar rotational movement may explain over-standing aesthetic and functional outcome, compared to classical genioplasty

Landmark-free morphometric analysis of knee osteoarthritis using joint statistical models of bone shape and articular space variability.

Purpose: Osteoarthritis (OA) is a common degenerative disease involving a variety of structural changes in the affected joint. In addition to narrowing of the articular space, recent studies involving statistical shape analysis methods have suggested that specific bone shapes might be associated with the disease. We aim to investigate the feasibility of using the recently introduced framework of functional shapes (Fshape) to extract morphological features of OA that combine shape variability of articular surfaces of the tibia (or femur) together with the changes of the joint space. Approach: Our study uses a dataset of three-dimensional cone-beam CT volumes of 17 knees without OA and 17 knees with OA. Each knee is then represented as an object (Fshape) consisting of a triangulated tibial (or femoral) articular surface and a map of joint space widths (JSWs) measured at the points of this surface (joint space map, JSM). We introduce a generative atlas model to estimate a template (mean) Fshape of the sample population together with template-centered variables that model the transformations from the template to each subject. This approach has two potential advantages compared with other statistical shape modeling methods that have been investigated in knee OA: (i)Fshapes simultaneously consider the variability in bone shape and JSW, and (ii)Fshape atlas estimation is based on a diffeomorphic transformation model of surfaces that does not require a priori landmark correspondences between the subjects. The estimated atlas-to-subject Fshape transformations were used as input to principal component analysis dimensionality reduction combined with a linear support vector machine (SVM) classifier to identify the morphological features of OA. Results: Using tibial articular surface as the shape component of the Fshape, we found leave-one-out cross validation scores of for the classification based on the bone surface transformations alone, for the classification based on the residual JSM, and for the classification using both Fshape components. Similar results were obtained using femoral articular surfaces. The discriminant directions identified in the statistical analysis were associated with medial narrowing of the joint space, steeper intercondylar eminence, and relative deepening of the medial tibial plateau. Conclusions: The proposed approach provides an integrated framework for combined statistical analysis of shape and JSPs. It can successfully extract features correlated to OA that appear consistent with previous studies in the field. Although future large-scale study is necessary to confirm the significance of these findings, our results suggest that the functional shape methodology is a promising new tool for morphological analysis of OA and orthopedics data in general.

FastCAT: Fast cone beam CT (CBCT) simulation.

To develop fastCAT, a fast cone-beam computed tomography (CBCT) simulator. fastCAT uses pre-calculated Monte Carlo (MC) CBCT phantom-specific scatter and detector response functions to reduce simulation time for megavoltage (MV) and kilovoltage (kV) CBCT imaging. Pre-calculated x-ray beam energy spectra, detector optical spread functions and energy deposition, and phantom scatter kernels are combined with GPU raytracing to produce CBCT volumes. MV x-ray beam spectra are simulated with EGSnrc for 2.5- and 6MeV electron beams incident on a variety of target materials and kV x-ray beam spectra are calculated analytically for an x-ray tube with a tungsten anode. Detectors were modeled in Geant4 extended by Topas and included optical transport in the scintillators. Two MV detectors were modeled-a standard Varian AS1200 GOS detector and a novel CWO high detective quantum efficiency detector. A kV CsI detector was also modeled. Energy-dependent scatter kernels were created in Topas for two 16cm diameter phantoms: A Catphan 515 contrast phantom and an anthropomorphic head phantom. The Catphan phantom contained inserts of 1-5mm in diameter of six different tissue types: brain, deflated lung, compact and cortical bone, adipose, and B-100. fastCAT simulations retain high fidelity to measurements and MC simulations: MTF curves were within 3.5% and 1.2% of measured values for the CWO and GOS detectors, respectively. HU values and CNR in a fastCAT Catphan 515 simulation were seen to be within 95% confidence intervals of an equivalent MC simulation for all of the tissues with root mean squared errors less than 16 HU and 1.6 in HU values and CNR comparisons, respectively. The anthropomorphic head phantom CWO detector CBCT image resulted in much higher tissue contrast and lower noise compared to the GOS detector CBCT image. A fastCAT simulation of the Catphan 515 module with an image size of 1024 1024 10 voxels took 61s on a GPU while the equivalent Topas MC was estimated to take more than 0.3CPU years. We present an open source fast CBCT simulation with high fidelity to MC simulations. The fastCAT python package can be found at https://github.com/jerichooconnell/fastCAT.git.

Cephalogram synthesis and landmark detection in dental cone-beam CT systems.

Due to the lack of a standardized 3D cephalometric analysis methodology, 2D cephalograms synthesized from 3D cone-beam computed tomography (CBCT) volumes are widely used for cephalometric analysis in dental CBCT systems. However, compared with conventional X-ray film based cephalograms, such synthetic cephalograms lack image contrast and resolution, which impairs cephalometric landmark identification. In addition, the increased radiation dose applied to acquire the scan for 3D reconstruction causes potential health risks. In this work, we propose a sigmoid-based intensity transform that uses the nonlinear optical property of X-ray films to increase image contrast of synthetic cephalograms from 3D volumes. To improve image resolution, super resolution deep learning techniques are investigated. For low dose purpose, the pixel-to-pixel generative adversarial network (pix2pixGAN) is proposed for 2D cephalogram synthesis directly from two cone-beam projections. For landmark detection in the synthetic cephalograms, an efficient automatic landmark detection method using the combination of LeNet-5 and ResNet50 is proposed. Our experiments demonstrate the efficacy of pix2pixGAN in 2D cephalogram synthesis, achieving an average peak signal-to-noise ratio (PSNR) value of 33.8 with reference to the cephalograms synthesized from 3D CBCT volumes. Pix2pixGAN also achieves the best performance in super resolution, achieving an average PSNR value of 32.5 without the introduction of checkerboard or jagging artifacts. Our proposed automatic landmark detection method achieves 86.7% successful detection rate in the 2mm clinical acceptable range on the ISBI Test1 data, which is comparable to the state-of-the-art methods. The method trained on conventional cephalograms can be directly applied to landmark detection in the synthetic cephalograms, achieving 93.0% and 80.7% successful detection rate in 4mm precision range for synthetic cephalograms from 3D volumes and 2D projections, respectively.

Automated contour propagation of the prostate from pCT to CBCT images via deep unsupervised learning.

To develop and evaluate a deep unsupervised learning (DUL) framework based on a regional deformable model for automated prostate contour propagation from planning computed tomography (pCT) to cone-beam CT (CBCT). We introduce a DUL model to map the prostate contour from pCT to on-treatment CBCT. The DUL framework used a regional deformable model via narrow-band mapping to augment the conventional strategy. Two hundred and fifty-one anonymized CBCT images from prostate cancer patients were retrospectively selected and divided into three sets: 180 were used for training, 12 for validation, and 59 for testing. The testing dataset was divided into two groups. Group 1 contained 50 CBCT volumes, with one physician-generated prostate contour on CBCT image. Group 2 contained nine CBCT images, each including prostate contours delineated by four independent physicians and a consensus contour generated using the STAPLE method. Results were compared between the proposed DUL and physician-generated contours through the Dice similarity coefficients (DSCs), the Hausdorff distances, and the distances of the center-of-mass. The average DSCs between DUL-based prostate contours and reference contours for test data in group 1 and group 2 consensus were 0.83±0.04, and 0.85±0.04, respectively. Correspondingly, the mean center-of-mass distances were 3.52mm±1.15mm, and 2.98mm±1.42mm, respectively. This novel DUL technique can automatically propagate the contour of the prostate from pCT to CBCT. The proposed method shows that highly accurate contour propagation for CBCT-guided adaptive radiotherapy is achievable via the deep learning technique.

A comparison of 2- and 3-dimensional mandibular superimposition techniques against Björk's structural superimposition method

The purpose of this research was to compare mandibular growth rotation relative to the cranial base in different vertical facial patterns on the basis of multiple 2-dimensional (2D) and 3-dimensional (3D) superimposition methods. Cone-beam computed tomography (CBCT) images taken at a mean interval of 54.8±16.8months were assessed from a sample of 70 growing patients. Three mandibular superimposition methods were compared against Björk's structural method: (1) a 2D landmark method (2D-M1), (2) a voxel-based 3D method based on a previously reported method (3D-M1), and (3) a voxel-based 3D method incorporating symphyseal structures as references (3D-M2). After superimposition, the relative change in cranial base lines as depicted in sagittal views were measured for true mandibular rotation. Agreement between methods was assessed with Lin's concordance correlation coefficient, Bland-Altman's limits of agreement, and the Bradley-Blackwood test. Lin's concordance correlation coefficients ranged between 0.924 for the 2D-M1 method, 0.695 for the 3D-M1 method, and 0.965 for the 3D-M2 method. Bland-Altman limits of agreement were wide for all but the 3D-M2 method. Finally, the Bradley-Blackwood test of equality of means and variances was significant in all except the 3D-M2 method. For time intervals between CBCT volume acquisitions>3years, the use of the 2D-M1 and 3D-M1 methods is not recommended. There was a high concordance between the 3D-M2 method and Björk's structural method when assessing mandibular growth rotation using relative changes in cranial base lines. The high concordance was displayed across all vertical facial types and for all time differences between first and second CBCT data acquisitions.

Effects of bicortical anchorage on pterygopalatine suture opening with microimplant-assisted maxillary skeletal expansion

The objectives of this study were to evaluate the effects of bicortical engagement by microimplants with maxillary skeletal expanders on pterygopalatine sutures opening and to analyze the postexpansion skeletal changes associated with it. Eighteen subjects treated with maxillary skeletal expanders were examined for pterygopalatine suture openings. Eight subjects who showed no evidence of the suture opening were assigned to the nonsplit group (NG), whereas 10 subjects with opened sutures were assigned to the split group (SG). Preexpansion and postexpansion cone-beam computed tomography images were superimposed for each group, and the changes in the 2 groups were compared. Finally, cone-beam computed tomography volumes were reoriented along the axis of each microimplant to check the bicortical engagement of the 4 microimplants. There was a significant correlation between the bicortical engagement of the orthodontic microimplants and the pterygopalatine suture opening (P=0.0003). In the NG, the average amount of transverse expansion measured at the center of resistance of the maxillary first molars, anterior nasal spine, and posterior nasal spine (PNS) was 4.33mm, 2.22mm, and 1.58mm, respectively, whereas the transverse expansion in the SG was 5.29mm, 2.21mm, and 2.46mm, respectively. The magnitude of transverse expansion at PNS was significantly higher in the SG than in the NG (P=0.036). The PNS also showed a significant anterior displacement in the SG (0.89mm) compared with the NG (0.06mm)(P=0.033). Bicortical microimplant anchorage is essential for pterygopalatine suture opening in microimplant-assisted maxillary skeletal expansion, which may result in further skeletal expansion and forward movement in the posterior part of the palatomaxillary complex.

Is periapical surgery follow-up with only two-dimensional radiographs reliable? A retrospective cohort type sensitivity study.

Background Two-dimensional (2D) radiographic techniques are commonly used for assessing lesion prognosis after endodontic surgery. The present retrospective cohort study analyzes the sensitivity and ability of different radiographic techniques in obtaining area and volume measurements of periapical lesions.Material and Methods Preoperative and follow-up (6-48 months) periapical and panoramic radiographs (index test) and cone-beam computed tomography (CBCT) images (reference standard) were selected from an endodontic microsurgery database. Sensitivity was analyzed independently by two examiners. The areas of the 2D radiographic images and CBCT volumes were studied using Itk-Snap software and Romexis viewer.Results The sample comprised 105 patients and 105 teeth, with a mean follow-up of 21 months (range 6-48). Preoperatively, CBCT detected all the periapical areas, periapical radiography detected 67, and panoramic radiography detected 60. Postoperatively, of the 52 cases in which CBCT detected remains of the periapical area, periapical radiography detected 22, and panoramic radiography detected 17. The measurements of the areas obtained by the 2D methods, and the volumes obtained by CBCT, had to be transformed into linear measures for comparison purposes. The measurements were found to be significantly different in both the preoperative and the follow-up images.Conclusions Periapical radiography showed greater sensitivity than panoramic radiography, both preoperatively and at follow-up. The lesions measured with CBCT were larger, with significant differences than as evidenced by the periapical and panoramic radiographs. Key words:Periapical radiography, panoramic radiography, CBCT, sensitivity, treatment outcome, size of periapical radiolucency, area, volume.

Anomalies of the clivus of interest in dental practice: A systematic review.

PurposeThe clivus is a region in the anterior section of the occipital bone that is commonly imaged on large-volume cone-beam computed tomography (CBCT). There have been several reports of incidental clivus variations and certain pathological entities that have been attributed to the variations. This study aimed to evaluate the effects of these variations within the scope of dentistry.Materials and MethodsMedical databases (PubMed, Scopus, and Web of Science) were searched using a controlled vocabulary (clival anomalies, cone-beam CT, canalis basilaris medianus, fossa navicularis magna, clival variation). The search was limited to English language, humans, and studies published in the last 25 years. The articles were exported into RefWorks® and duplicates were removed. The remaining articles were screened and reviewed for supporting information on variations of the clivus on CBCT imaging.ResultsCanalis basilaris medianus and fossa navicularis magna were the most common anomalies noted. Many of these variations were asymptomatic, with most patients unaware of the anomaly. In certain cases, associated pathologies ranged from developmental (Tornwaldt cyst), to acquired (recurrent meningitis). While no distinct pathognomonic aspects were noted, there were unique patterns of radiographic diagnosis and treatment modalities. Most patients had a normal course of follow-up.ConclusionInterpretation of CBCT volumes is a skill every dentist must possess. When reviewing large-volume CBCT scans, the clinician should be able to distinguish pathology from normal anatomic variations within the skull base. The majority of clivus variations are asymptomatic and will remain undetected unless incidentally noted on radiographic examinations.