Cone-beam Computed Tomography Volume Research Articles

Scout images acquired prior to cone beam CT acquisitions: reproducibility of findings and added diagnostic information.

The aim of the present study was to assess the reproducibility of findings in cone beam computed tomography (CBCT) scout images. Furthermore, the study aimed to assess whether a scout image shows pathology not seen within the CBCT volume (ie, added diagnostic information) and therefore must be assessed on the same terms as the full volume. Using a retrospective design, 233 CBCT reports and scout images were assessed. Kappa statistics and percentage of accordance were used to evaluate intra- and interobserver reproducibility as well as agreement between scout and CBCT report. Intra- and interobserver reproducibility were overall low (kappa ranging from -0.008 to 1.000). Agreement between findings reported in the CBCT and scout was also low. One-hundred-fourteen impacted teeth, one apical periodontitis, and two sinus conditions seen in the scout image were not registered in the full volume report due to the extended size of the scout image. Reproducibility of findings in scout images compared to CBCT volumes was low, and the scout showed very little additional diagnostic information. This study shows that although the reproducibility of viewing scout images is low, rare findings can go undetected if the scout is not assessed. Legislation regarding interpretation of scout images needs to be discussed.

A Novel Approach of Surface Texture Mapping for Cone-Beam Computed Tomography in Image-Guided Surgical Navigation.

The demand for cone-beam computed tomography (CBCT) imaging in clinics, particularly in dentistry, is rapidly increasing. Preoperative surgical planning is crucial to achieving desired treatment outcomes for imaging-guided surgical navigation. However, the lack of surface texture hinders effective communication between clinicians and patients, and the accuracy of superimposing a textured surface onto CBCT volume is limited by dissimilarity and registration based on facial features. To address these issues, this study presents a CBCT imaging system integrated with a monocular camera for reconstructing the texture surface by mapping it onto a 3D surface model created from CBCT images. The proposed method utilizes a geometric calibration tool for accurate mapping of the camera-visible surface with the mosaic texture. Additionally, a novel approach using 3D-2D feature mapping and surface parameterization technology is proposed for texture surface reconstruction. Experimental results, obtained from both real and simulation data, validate the effectiveness of the proposed approach with an error reduction to 0.32 mm and automated generation of integrated images. These findings demonstrate the robustness and high accuracy of our approach, improving the performance of texture mapping in CBCT imaging.

Prognosis recovery score of apical surgery Guided Bone Regeneration using cone beam computed tomography and digital bioinformatics

ObjectiveGuided Bone Regeneration (GBR) is a dental surgical procedure that uses barrier membranes to prevent soft tissue invasion and conduct new bone growth. This study aimed to define a Prognosis Recovery score (PR score) to objectively classify post-surgery responders from non-responder patients who underwent GBR using Cone Beam Computed Tomography (CBCT). MethodsThis prospective-observational-longitudinal-cohort study recruited 250 individuals who were assigned to: Conventional-Apical-Surgery (CAS, n = 39), Apical-Surgery using human fascia lata Membrane placement (ASM, n = 42), and Apical-Surgery using human fascia lata Membrane placement and lyophilized allograft Bone powder (ASMB, n = 39); and Apical-Surgery using collagen membrane Porcine origin and Bovine Bone-matrix (ASPBB, n = 130), an independent external validation cohort. Surgery was performed, and evolution was monitored by CBCTs at 0, 6-, 12-, 18-, and 24 months post-surgery. ResultsNormalized lesion volumes were calculated, and non-linear time evolution morphology curves were characterized. The three-time evolution bone growth patterns were: a linear tendency (PR0), “S'' shaped log-logistic (PR1), and “C" cellular growth (PR2). The treatment success rates were PR2-46 %, PR2-88 %, and PR2-95 %/PR1-5% for CAS, ASM, and ASMB groups. The xenograft ASPBB counterpart achieved PR2-92 % and PR1-8%. The score PR had a sensitivity, specificity, and accuracy of 100 %. ConclusionsPatients' treatment success can be quantitatively, objectively, and precisely predicted with the Prognosis Recovery score (using only two CBCTs), according to their biological response to allograft or xenograft materials (time-evolution bone growth curves), reducing cost and radiation exposure. Clinical significanceThrough digital imaging and bioinformatic analysis of bone regeneration observed in CBCTs, we defined a Prognosis Recovery (PR) score using only two CBCT volume assessments (0 and 6 months). The PR score allowed us to define three time-evolution curves depending on the biomaterials used and to classify patients in a quantitative, objective, and accurate way.

Dynamic CBCT imaging using prior model-free spatiotemporal implicit neural representation (PMF-STINR)

Objective. Dynamic cone-beam computed tomography (CBCT) can capture high-spatial-resolution, time-varying images for motion monitoring, patient setup, and adaptive planning of radiotherapy. However, dynamic CBCT reconstruction is an extremely ill-posed spatiotemporal inverse problem, as each CBCT volume in the dynamic sequence is only captured by one or a few x-ray projections, due to the slow gantry rotation speed and the fast anatomical motion (e.g. breathing). Approach. We developed a machine learning-based technique, prior-model-free spatiotemporal implicit neural representation (PMF-STINR), to reconstruct dynamic CBCTs from sequentially acquired x-ray projections. PMF-STINR employs a joint image reconstruction and registration approach to address the under-sampling challenge, enabling dynamic CBCT reconstruction from singular x-ray projections. Specifically, PMF-STINR uses spatial implicit neural representations to reconstruct a reference CBCT volume, and it applies temporal INR to represent the intra-scan dynamic motion of the reference CBCT to yield dynamic CBCTs. PMF-STINR couples the temporal INR with a learning-based B-spline motion model to capture time-varying deformable motion during the reconstruction. Compared with the previous methods, the spatial INR, the temporal INR, and the B-spline model of PMF-STINR are all learned on the fly during reconstruction in a one-shot fashion, without using any patient-specific prior knowledge or motion sorting/binning. Main results. PMF-STINR was evaluated via digital phantom simulations, physical phantom measurements, and a multi-institutional patient dataset featuring various imaging protocols (half-fan/full-fan, full sampling/sparse sampling, different energy and mAs settings, etc). The results showed that the one-shot learning-based PMF-STINR can accurately and robustly reconstruct dynamic CBCTs and capture highly irregular motion with high temporal (∼ 0.1 s) resolution and sub-millimeter accuracy. Significance. PMF-STINR can reconstruct dynamic CBCTs and solve the intra-scan motion from conventional 3D CBCT scans without using any prior anatomical/motion model or motion sorting/binning. It can be a promising tool for motion management by offering richer motion information than traditional 4D-CBCTs.

Evaluating free segmentation tools for CBCT-derived models: Cost-effective solutions.

This study evaluated the segmentation accuracy and reliability of free software packages and compared them with commercial alternatives. A total of 36 stone models were scanned using a desktop scanner and then imaged by cone beam computed tomography (CBCT). The CBCT volumes were segmented using 2 free software packages (3D Slicer and Blue Sky Plan) and 2 commercial software packages (Mimics and OnDemand3D). Stereolithography (STL) files generated by the desktop scanner were used as the control group (reference models). The accuracy of segmentation was evaluated by (1) comparing 6 linear measurements taken from each STL model generated by the 4 software packages with that obtained by the scanner, and (2) deviation analysis of each STL model generated by the 4 software packages with that obtained by the scanner. Absolute error and percentage error, repeated measures anova and Friedman test followed by post hoc analysis, intraclass correlation coefficient (ICC), and Pearson's r were used to evaluate the accuracy of the tested software packages. There was no statistically significant difference in all intra-arch measurements obtained using the four software packages. Measurements obtained using the free software packages and the scanner showed excellent positive correlation, ranging from 0.825 to 0.988, confirming equivalence with commercial software packages. Within the settings of the current study, accurate and time-saving segmentations with high positive correlation could be performed using the tested free segmentation software packages (3D Slicer and Blue Sky Plan). Nevertheless, further evaluation is necessary to gage their accuracy using different CBCT modalities.

Impact of acquisition volume on cone beam computed tomography imaging of marginal bone: an ex vivo study.

The current study explores whether there is a clinically relevant distinction in the measurement of marginal bone loss when comparing high-dose (HD) versus low-dose (LD) cone beam computed tomography (CBCT) protocols in small and large acquisition volumes. Material and Methods: CBCTs of four human cadaveric preparates were taken in HD and LD mode in two different fields of view 8 × 8 cm2 (LV) and 5 × 5 cm2 (SV). In total, 43 sites of 15 teeth were randomly chosen, and marginal bone loss was measured twice in all protocols at 43 sites of 15 teeth by one calibrated investigator. Bland-Altman plots and Lin's concordance correlation coefficient (CCC) were calculated to assess the extent of agreement of the measurements. Additionally, the rater scored the certainty in each of the measurements. For HD-CBCT CCC of measurements obtained using SV versus LV was 0.991. CCC of measurements obtained using SV versus LV of LD-CBCT was 0.963. Both CCC values indicated excellent agreement between the two volumes in both protocols. CCC also indicated high intramodality correlation between HD-CBCT and LD-CBCT independent of the acquisition volume (0.963 - 0.992). Bland-Altman plots also indicated no substantial differences. Results of certainty scoring showed significant differences (p = 0.004 (LV), p < 0.001(SV)) between the LD and HD-CBCT. Accuracy of measurements of bone loss shows no clinical noticeable effects depending on the CBCT volume in this ex vivo study. There appears to be no relevant advantage of SV over LV, neither in HD-CBCT nor in LD-CBCT and additionally no relevant advantage of HD versus LD in visualizing marginal bone loss.

Feasibility of frozen soft tissues to simulate fresh soft tissue conditions in cone beam CT scans.

To evaluate the feasibility of frozen soft tissues in simulating fresh soft tissues of pig mandibles using cone beam CT (CBCT). Two fresh pig mandibles with soft tissues containing 2 tubes filled with a radiopaque homogeneous solution were scanned using 4 CBCT units and 2 field-of-view (FOV) sizes each. The pig mandibles were deep-frozen and scanned again. Three cross-sections were exported from each CBCT volume and grouped into pairs, with one cross-section representing a fresh and one a frozen mandible. Three radiologists compared the pairs and attributed a score to assess the relative image quality using a 5-point scale. Mean grey values and standard deviation were obtained from homogeneous areas in the tubes, compared using the Wilcoxon matched-pair signed-rank test and subjected to Pearson correlation analysis between fresh and frozen physical states (α = .05). Subjective evaluation revealed similarity of the CBCT image quality between fresh and frozen states. The distribution of mean grey values was similar between fresh and frozen states. Mean grey values of the frozen state in the small FOV were significantly greater than those of the fresh state (P = .037), and noise values of the frozen state in the large FOV were significantly greater than those of the fresh state (P = 0.007). Both mean grey values and noise exhibited significant and positive correlations between fresh and frozen states (P < 0.01). The freezing of pig mandibles with soft tissues may serve as a method to prolong their usability and working time when CBCT imaging is planned.

Cone Beam Computed Tomography Evaluation of Median Mandibular Lingual Canal in a Western Indian Subpopulation: A Cross-sectional Study

Introduction: The Mandibular Lingual Canals (MLC), which carry the sublingual artery, are present in the mandible near the genial tubercle. Identification of these canals is important before any dental surgical procedures. Aim: To identify and evaluate the mandibular lingual canal in Cone Beam Computed Tomography (CBCT) of the Western Indian Population. Materials and Methods: The current cross-sectional study was conducted at the Department of Oral Medicine and Radiology, Sumandeep Vidyapeeth, Vadodara, Gujarat, India between October 2022 and June 2023. A total of 100 CBCT volumes showing the mandibular anterior region were evaluated for the presence or absence of a mandibular lingual canal. The canals were further evaluated for their number, diameter, length, thickness of the buccal cortical plate, and the remaining bone above and below the canal. The evaluation was done by two separate investigators. The data were entered into Microsoft Excel and subjected to statistical analysis. Data collected were tested statistically using the Chi-square test. Results: A total of 59 males and 41 females’ mandibular CBCT volumes were evaluated, out of which 93 CBCT volumes showed the mandibular lingual canal. The maximum number (n=57) of individuals showed three canals, followed by two canals (n=26) and one canal (n=10). There was no statistical significance between genders regarding canal number, diameter, length, thickness of the buccal cortical plate, and the remaining bone above and below the canal. Conclusion: Three-canal morphology was the most common anatomical form of MLC found in the Western Indian population.

Clinical validity and precision of deep learning-based cone-beam computed tomography automatic landmarking algorithm.

This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which were determined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The time required to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually, compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculate these measurements, the efficiency of diagnosis and treatment may be improved.

Simulation-driven training of vision transformers enables metal artifact reduction of highly truncated CBCT scans.

Due to the high attenuation of metals, severe artifacts occur in cone beam computed tomography (CBCT). The metal segmentation in CBCT projections usually serves as a prerequisite for metal artifact reduction (MAR)algorithms. The occurrence of truncation caused by the limited detector size leads to the incomplete acquisition of metal masks from the threshold-based method in CBCT volume. Therefore, segmenting metal directly in CBCT projections is pursued in thiswork. Since the generation of high quality clinical training data is a constant challenge, this study proposes to generate simulated digital radiographs (dataI) based on real CT data combined with self-designed computer aided design (CAD) implants. In addition to the simulated projections generated from 3D volumes, 2D x-ray images combined with projections of implants serve as the complementary data set (dataII) to improve the network performance. In this work, SwinConvUNet consisting of shift window (Swin) vision transformers (ViTs) with patch merging as encoder is proposed for metalsegmentation. The model's performance is evaluated on accurately labeled test datasets obtained from cadaver scans as well as the unlabeled clinical projections. When trained on the dataI only, the convolutional neural network (CNN) encoder-based networks UNet and TransUNet achieve only limited performance on the cadaver test data, with an average dice score of 0.821 and 0.850. After using both dataII and dataI during training, the average dice scores for the two models increase to 0.906 and 0.919, respectively. By replacing the CNN encoder with Swin transformer, the proposed SwinConvUNet reaches an average dice score of 0.933 for cadaver projections when only trained on the dataI. Furthermore, SwinConvUNet has the largest average dice score of 0.953 for cadaver projections when trained on the combined dataset. Our experiments quantitatively demonstrate the effectiveness of the combination of the projections simulated under two pathways for network training. Besides, the proposed SwinConvUNet trained on the simulated projections performs state-of-the-art, robust metal segmentation as demonstrated on experiments on cadaver and clinical data sets. With the accurate segmentations from the proposed model, MAR can be conducted even for highly truncated CBCTscans.

Clinical Impact of a Protocol Involving Cone-Beam CT (CBCT), Fusion Imaging and Ablation Volume Prediction in Percutaneous Image-Guided Microwave Ablation in Patients with Hepatocellular Carcinoma Unsuitable for Standard Ultrasound (US) Guidance.

to evaluate the clinical impact of a protocol for the image-guided percutaneous microwave ablation (MWA) of hepatocellular carcinoma (HCC) that includes cone-beam computed tomography (CBCT), fusion imaging and ablation volume prediction in patients with hepatocellular carcinoma unsuitable for standard ultrasound (US) guidance. this study included all patients with HCC treated with MWA between January 2021 and June 2022 in a tertiary institution. Patients were divided into two groups: Group A, treated following the protocol, and Group B, treated with standard ultrasound (US) guidance. Follow-up images were reviewed to assess residual disease (RD), local tumor progression (LTP) and intrahepatic distant recurrence (IDR). Ablation response at 1 month was also evaluated according to mRECIST. Baseline variables and outcomes were compared between the groups. For 1-month RD, propensity score weighting (PSW) was performed. 80 consecutive patients with 101 HCCs treated with MWA were divided into two groups. Group A had 41 HCCs in 37 patients, and Group B had 60 HCCs in 43 patients. Among all baseline variables, the groups differed regarding their age (mean of 72 years in Group A and 64 years in Group B, respectively), new vs. residual tumor rates (48% Group A vs. 25% Group B, p < 0.05) and number of subcapsular tumors (56.7% Group B vs. 31.7% Group A, p < 0.05) and perivascular tumors (51.7% Group B vs. 17.1% Group A, p < 0.05). The protocol led to repositioning the antenna in 49% of cases. There was a significant difference in 1-month local response between the groups measured as the RD rate and mRECIST outcomes. LTP rates at 3 and 6 months, and IDR rates at 1, 3 and 6 months, showed no significant differences. Among all variables, logistic regression after PSW demonstrated a protective effect of the protocol against 1-month RD. The use of CBCT, fusion imaging and ablation volume prediction during percutaneous MWA of HCCs provided a better 1-month tumor local control. Further studies with a larger population and longer follow-up are needed.

Do voxel size and filter application on CBCT images improve the diagnosis of condylar morphological alterations? An exvivo study.

There is no established protocol for the low doses of cone-beam computed tomography (CBCT) acquisition and filter application on the diagnosis of condylar morphological alterations. The objective of the study was to evaluate the influence of voxel size and filter application on the diagnosis of condylar morphological alterations in CBCT using an exvivo model. CBCT scans of 36 temporomandibular joints were acquired using OP300 Maxio with voxel sizes of 0.085, 0.125 and 0.280 mm. Three radiologists evaluated the condyles in the CBCT volumes under three filter modes: no filter, '1x' sharpen filter and '2x' sharpen filter. The area under the ROC curve, sensitivity and specificity were calculated by comparing the evaluators' responses with the reference standard and compared among experimental groups using analysis of variance (ANOVA) (α = 5%). The area under the ROC curve, sensitivity and specificity were not affected by voxel size and filter application (p > .05). For osteophyte and flattening, there were more true-positive responses in smaller voxel size. For erosion, the increase of true- and false-positive responses occurred with a larger voxel size. Overall, there was a tendency to decrease true positives for osteophyte and erosion and increase false positives for flattening with the '2x' sharpen filter. The diagnosis of condylar morphological alterations assessed in this exvivo analysis is not influenced by the voxel size and the application of the filters. However, attention is needed with the occurrence of false-positive diagnosis with a larger voxel size for erosion and '2x' sharpen filter for flattening.

End-to-end memory-efficient reconstruction for cone beamCT.

Cone beam computed tomography (CBCT) plays an important role in many medical fields nowadays. Unfortunately, the potential of this imaging modality is hampered by lower image quality compared to the conventional CT, and producing accurate reconstructions remains challenging. A lot of recent research has been directed towards reconstruction methods relying on deep learning, which have shown great promise for various imaging modalities. However, practical application of deep learning to CBCT reconstruction is complicated by several issues, such as exceedingly high memory costs of deep learning methods when working with fully 3D data. Additionally, deep learning methods proposed in the literature are often trained and evaluated only on data from a specific region of interest, thus raising concerns about possible lack of generalization to other regions. In this work, we aim to address these limitations and propose LIRE: a learned invertible primal-dual iterative scheme for CBCTreconstruction. LIRE is a learned invertible primal-dual iterative scheme for CBCT reconstruction, wherein we employ a U-Net architecture in each primal block and a residual convolutional neural network (CNN) architecture in each dual block. Memory requirements of the network are substantially reduced while preserving its expressive power through a combination of invertible residual primal-dual blocks and patch-wise computations inside each of the blocks during both forward and backward pass. These techniques enable us to train on data with isotropic 2 mm voxel spacing, clinically-relevant projection count and detector panel resolution on current hardware with 24 GBvideo random access memory (VRAM). Two LIRE models for small and for large field-of-view (FoV)setting were trained and validated on a set of 260 + 22 thorax CT scans and tested using a set of 142 thorax CT scans plus an out-of-distribution dataset of 79 head and neck CT scans. For both settings, our method surpasses the classical methods and the deep learning baselines on both test sets. On the thorax CT set, our method achieves peak signal-to-noise ratio (PSNR) of 33.84 ± 2.28 for the small FoV setting and 35.14 ± 2.69 for the large FoV setting; U-Net baseline achieves PSNR of 33.08 ± 1.75 and 34.29 ± 2.71 respectively. On the head and neck CT set, our method achieves PSNR of 39.35 ± 1.75 for the small FoV setting and 41.21 ± 1.41 for the large FoV setting; U-Net baseline achieves PSNR of 33.08 ± 1.75 and 34.29 ± 2.71 respectively. Additionally, we demonstrate that LIRE can be finetuned to reconstruct high-resolution CBCT data with the same geometry but 1 mm voxel spacing and higher detector panel resolution, where it outperforms the U-Net baseline aswell. Learned invertible primal-dual schemes with additional memory optimizations can be trained to reconstruct CBCT volumes directly from the projection data with clinically-relevant geometry and resolution. Such methods can offer better reconstruction quality and generalization compared to classical deep learning baselines.

Evaluation of deep learning for detecting intraosseous jaw lesions in cone beam computed tomography volumes

The study aim was to develop and assess the performance of a deep learning (DL) algorithm in the detection of radiolucent intraosseous jaw lesions in cone beam computed tomography (CBCT) volumes. A total of 290 CBCT volumes from more than 12 different scanners were acquired. Fields of view ranged from 6 × 6 × 6 cm to 18 × 18 × 16 cm. CBCT volumes contained either zero or at least one biopsy-confirmed intraosseous lesion. 80 volumes with no intraosseous lesions were included as controls and were not annotated. 210 volumes with intraosseous lesions were manually annotated using ITK-Snap 3.8.0. 150 volumes (10 control, 140 positive) were presented to the DL software for training. Validation was performed using 60 volumes (30 control, 30 positive). Testing was performed using the remaining 80 volumes (40 control, 40 positive). The DL algorithm obtained an adjusted sensitivity by case, specificity by case, positive predictive value by case, and negative predictive value by case of 0.975, 0.825, 0.848, and 0.971, respectively. A DL algorithm showed moderate success at lesion detection in their correct locations, as well as recognition of lesion shape and extent. This study demonstrated the potential of DL methods for intraosseous lesion detection in CBCT volumes.

Accuracy of Anatomical Depictions in Cone Beam Computed Tomography (CBCT)-Reconstructed Panoramic Projections Compared to Conventional Panoramic Radiographs: A Clinical Risk-Benefit Analysis.

Two-dimensional (2D) radiographs are the standard of care for diagnosis and treatment planning in the day-to-day practice of dentistry. With the growing popularity of cone beam computed tomography (CBCT), it is now becoming the standard of care in many areas of general dentistry due to its ability to create non-linear projections from volumetric data. The CBCT-generated non-orthogonal radiographs can serve as easy-to-use 2D and three-dimensional (3D) diagnostic tools and offer a similar experience for diagnosis as conventional 2D images. The aim of this study is to compare the accuracy of conventional radiographs and CBCT-generated projections to identify relevant anatomic landmarks and their associated variants. Thirty-two patients referred to the University of Connecticut School of Dental Medicine's Advanced Imaging Center were selected for this retrospective analysis. Nineteen anatomical landmarks were retrospectively assessed on conventional panoramic and CBCT scans generated panoramic radiographs using two different digital imaging and communications in medicine viewers. A total of 1,216 anatomical landmarks were evaluated by two oral and maxillofacial radiologists to assess the accuracy and consistency of the depiction of radiographic anatomy. There was a very good agreement between the two evaluators with a Cohen's kappa value of 0.934. McNemar change test concluded that the anatomical assessment values compared between conventional panoramic and CBCT-generated panoramic radiographs are similar. This study showed that CBCT-generated panoramic images are comparable to conventional panoramic radiographs in identifying anatomical landmarks typically evaluated using a conventional panoramic projection. In addition, they have the added advantage of having 3D information in the acquired volume to better evaluate the area of interest. In clinical situations where a mid- to large-volume CBCT scan is available, a simulated panoramic image can be generated using the CBCT volume, leaving exposure of the patient to the additional radiation of a panoramic image unnecessary.

High-resolution CBCT assessing a maxillary central incisor with root bifurcation: A case report

Cone beam computed tomography (CBCT) is an imaging modality widely used in endodontics, since it provides three-dimensional images without superposition of structures. The maxillary central incisor usually presents a single root and one root canal, but anatomical variations can happen. The aim of the present paper is to report a high-resolution CBCT evaluation of an upper central incisor with root bifurcation. This paper shows a descriptive and qualitative assessment of high-resolution CBCT images for endodontic purposes. A 39-year-old male patient was referred to a radiologic clinic to acquire CBCT images of the right upper central incisor, as the referring clinician noticed an anatomical difference between the two upper central incisors on a panoramic radiograph and suspected of dens invaginatus. CBCT images were acquired in high resolution (voxel size of 0.08 mm) and a restricted field-of-view (4x4 cm). CBCT volume displayed an anatomical variation of the central incisor, which presented root bifurcation and two root canals, with a normal clinical crown. The present case report shows a rare diagnosis of a maxillary central incisor presenting bifurcated root with two root canals, and to the best of the authors’ knowledge, this is the first case report to present high-resolution CBCT images with disclosed acquisition protocols to diagnose a bifurcated central incisor. The documentation of unusual cases has great didact value and contributes to information propagation. In the present case, high-resolution CBCT allowed a detailed evaluation of both roots and root canals, facilitating the clinician’s diagnostic task.

Detection of extracranial and intracranial calcified carotid artery atheromas in cone beam computed tomography using a deep learning convolutional neural network image segmentation approach

We leveraged an artificial intelligence deep-learning convolutional neural network (DL CNN) to detect calcified carotid artery atheromas (CCAAs) on cone beam computed tomography (CBCT) images. We obtained 137 full-volume CBCT scans with previously diagnosed CCAAs. The DL model was trained on 170 single axial CBCT slices, 90 with extracranial CCAAs and 80 with intracranial CCAAs. A board-certified oral and maxillofacial radiologist confirmed the presence of each CCAA. Transfer learning through a U-Net-based CNN architecture was utilized. Data allocation was 60% training, 10% validation, and 30% testing. We determined the accuracy of the DL model in detecting CCAA by calculating the mean training and validation accuracy and the area under the receiver operating characteristic curve (AUC). We reserved 5 randomly selected unseen full CBCT volumes for final testing. The mean training and validation accuracy of the model in detecting extracranial CCAAs was 92% and 82%, respectively, and the AUC was 0.84 with 1.0 sensitivity and 0.69 specificity. The mean training and validation accuracy in detecting intracranial CCAAs was 61% and 70%, respectively, and the AUC was 0.5 with 0.93 sensitivity and 0.08 specificity. Testing of full-volume scans yielded an AUC of 0.72 and 0.55 for extracranial and intracranial CCAAs, respectively. Our DL model showed excellent discrimination in detecting extracranial CCAAs on axial CBCT images and acceptable discrimination on full-volumes but poor discrimination in detecting intracranial CCAAs, for which further research is required.

Feature-Oriented CBCT Self-Calibration Parameter Estimator for Arbitrary Trajectories: FORCAST-EST

Background: For the reconstruction of Cone-Beam CT volumes, the exact position of each projection is needed; however, in some situations, this information is missing. Purpose: The development of a self-calibration algorithm for arbitrary CBCT trajectories that does not need initial positions. Methods: Projections are simulated in a spherical grid around the center of rotation. Through using feature detection and matching, an acquired projection is compared to each simulated image in this grid. The position with the most matched features was used as a starting point for a fine calibration with a state-of-the-art algorithm. Evaluation: This approach is compared with the calibration of nearly correct starting positions when using FORCASTER and CMA-ES minimization with a normalized gradient information (NGI) objective function. The comparison metrics were the normalized root mean squared error, structural similarity index, and the dice coefficient, which were evaluated on the segmentation of a metal object. Results: The parameter estimation for a regular Cone-Beam CT with a 496 projection took 1:26 h with the following metric values: NRMSE = 0.0669; SSIM = 0.992; NGI = 0.75; and Dice = 0.96. FORCASTER with parameter estimation took 3:28 h with the following metrics: NRMSE = 0.0190; SSIM = 0.999; NGI = 0.92; and Dice = 0.99. CMA-ES with parameter estimation took 5:39 h with the following metrics: NRMSE = 0.0037; SSIM = 1.0; NGI = 0.98; and Dice = 1.0. Conclusions: The proposed algorithm can determine the parameters of the projection orientations for arbitrary trajectories with enough accuracy to reconstruct a 3D volume with low errors.

Factors affecting the maxillary and mandibular incisors’ buccolingual inclinations and buccal and lingual cortical plate heights

IntroductionOrthodontics is closely related to periodontics. The buccolingual inclination (BLI) of the incisors and deficiencies in their buccal (BHd) and lingual (LHd) cortical plate heights may affect orthodontic outcomes. Identifying risk factors that can compromise buccal or lingual bone heights may have clinical value. The literature on BLI/BHd/LHd is not only scarce but also limited to one jaw. We aimed to examine, for the first time, factors affecting BLI/BHd/LHd not evaluated before as well as other factors with scarce literature about them.MethodsIn this two-phase epidemiological and analytical study, inclinations and cortical heights of 248 incisors (bilateral centrals and laterals) were evaluated blindly on 62 randomly selected high-resolution pretreatment cone-beam computed tomography volumes (30 maxillae [13 men, 17 women], 32 mandibles [13 men, 19 women]). The sample was balanced in terms of sexes, jaws, and ages. The BLI/BHd/LHd of bilateral incisors were measured (intraobserver agreement > 95%). The effects of jaws, sexes, age, sides, and incisor types on each of the anatomical variables (BLI/BHd/LHd) were analyzed using a Mixed-Model Multiple Linear Regression analysis. Correlations among continuous variables were assessed using a Pearson coefficient (α = 0.05).ResultsFor the maxillary centrals, BLI, BHd, and LHd were 106.79 ± 5.06, 1.94 ± 0.95, and 1.50 ± 0.76, respectively. These parameters were ‘110.56 ± 5.97, 1.81 ± 0.83, 1.23 ± 0.69’ for the maxillary laterals; ‘97.64 ± 8.26, 2.98 ± 1.48, 3.46 ± 1.45’ for the mandibular centrals; and ‘95.98 ± 6.80, 3.29 ± 1.72, and 2.73 ± 1.15’ for the mandibular laterals. BLI was greater in the maxilla compared to the mandible and in the lateral incisors compared to centrals (P < 0.05). BHd was greater (more deficient) in the mandible (P = 0.000). Age, sex, or side were not associated with BLI (P > 0.05). Age, sex, side, or incisor types were not associated with BHd (P > 0.05). LHd was greater in the mandible, older individuals, and centrals (P < 0.05). There were some significant but weak correlations between BLI with BHd and especially LHd (P < 0.05).ConclusionIn the maxilla, but not in the mandible, incisors’ BLI may determine LHd. Maxillary incisors may have greater BLIs as well as greater buccal and lingual alveolar bone heights compared to mandibular incisors. BLI might be greater in the laterals compared to the centrals in both jaws combined.

Semi-automatic 3D reconstruction of middle and inner ear structures using CBCT

ABSTRACT We present a semi-automatic reconstruction approach of middle and inner ear structures using generic 3D deformable surface models from Cone Beam CT (CBCT) examination. First, the user must position a set of control points in the CBCT volume for each of the 4 structures of the inner and middle ear. These points are used to position the deformable surface models and to customize them so that they are as close to the boundaries as possible. Finally, each mesh is refined iteratively segmenting the limits of the structure while taking into account neighbouring structures as boundary constraints. Our method is tested on left and right ears of 20 scans of patients analysed retrospectively. The results show the efficiency and reliability of this approach with an average Dice Similarity Coefficient of 91.8% for the inner ear model and 89.9% for the ossicular chain and a total reconstruction time of 5 minutes. The implementation of our method in a clinical setting could provide clinicians with distinct and accurate 3D models of the ear structures without requiring a tedious manual segmentation step, in order to give them a better understanding of the auditory system in vivo and help them in diagnosis and follow-up.