Cone-beam Computed Tomography Volume Research Articles

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.

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.

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.

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.

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.

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.

Cone Beam Computed Tomography (CBCT) Evaluation of Alveolar Bone Thickness and Root Angulation in Anterior Maxilla for Planning Immediate Implant Placement.

This retrospective study aimed to measure the labial, palatal, mesial, and distal bone thickness around maxillary central and lateral incisors and canines and height from crest to apex, using cone beam computed tomography (CBCT) images and compare the results based on gender. The second objective of the study was to measure root angulation on CBCT images and its relation with the labial cortical thicknesses. Material and Methods:After the Institutional Review Board (IRB) approval, a total of 140 CBCT volumes were included in this study according to the set criteria. On each scan, right-side maxillary central, lateral incisors, and canine were selected for the measurements. All the measurements were done at three levels at the alveolar crest (L1), mid-root (L2), and apical region (L3) for each tooth. Results:The Student's t-test was performed to compare the result of buccal, palatal, mesial, and distal bone thickness,angulation, and height of all subjects. Buccal alveolar bone thickness was minimum at the mid-root region, and the palatal bone thickness was minimum at the crestal region. The mesial bone thickness was minimum at the mid-root level, and distalbone thickness was minimum at the crest level. The available bone height was maximum at the lateral incisor and equal for the central incisor and canine. The canine was the most angulated tooth. Cone beam computed tomography is a reliable imaging modality to evaluate pre-surgical immediate implant sites and measurealveolar bone thickness. The canine was the most angulated tooth with more buccal alveolar bone thickness.

Comparative study of cranial measurements between sexes from Brazil and The Netherlands: A cone-beam computed tomography study.

The objective of this study was to better understand human variation by comparing cone-beam computed tomography-based cranial measurements between both sexes of individuals from two distinct populations: Brazilian and Dutch. Cone-beam computed tomography volumes of 311 patients between 20 and 60 years from Brazil and The Netherlands were selected. Two radiologists performed 16 linear measurements in the maxillary sinuses and mandibular canal. Kruskall-Wallis test compared measurements of the two cranial structures between male and female for the two populations and four age ranges (20-30, 31-40, 41-50, 51-60). Mann-Whitney test compared individual measurements obtained from the cranial structures between male and female for each population, and between both populations for both sexes. Intra- and inter-observer reliability was assessed by intraclass correlation test (α=0.05). No significant differences were found in the linear measurements among the experimental groups including sex, population and age group for both cranial structures (p > 0.05). Most of the cranial linear measurements were significantly higher for male than those for female irrespective of the population (p ≤ 0.05). When the populations were compared regardless of sex, Brazilians presented four significantly higher measurements, and Dutch presented seven significantly higher measurements (p ≤ 0.05). The assessed cranial structures did not differ between Brazilian and Dutch populations for both sexes and four age ranges. Multiple linear measurements differed between both populations with a predominance of larger dimensions for the Dutch population.

Inferior Alveolar Canal Automatic Detection with Deep Learning CNNs on CBCTs: Development of a Novel Model and Release of Open-Source Dataset and Algorithm

Introduction: The need of accurate three-dimensional data of anatomical structures is increasing in the surgical field. The development of convolutional neural networks (CNNs) has been helping to fill this gap by trying to provide efficient tools to clinicians. Nonetheless, the lack of a fully accessible datasets and open-source algorithms is slowing the improvements in this field. In this paper, we focus on the fully automatic segmentation of the Inferior Alveolar Canal (IAC), which is of immense interest in the dental and maxillo-facial surgeries. Conventionally, only a bidimensional annotation of the IAC is used in common clinical practice. A reliable convolutional neural network (CNNs) might be timesaving in daily practice and improve the quality of assistance. Materials and methods: Cone Beam Computed Tomography (CBCT) volumes obtained from a single radiological center using the same machine were gathered and annotated. The course of the IAC was annotated on the CBCT volumes. A secondary dataset with sparse annotations and a primary dataset with both dense and sparse annotations were generated. Three separate experiments were conducted in order to evaluate the CNN. The IoU and Dice scores of every experiment were recorded as the primary endpoint, while the time needed to achieve the annotation was assessed as the secondary end-point. Results: A total of 347 CBCT volumes were collected, then divided into primary and secondary datasets. Among the three experiments, an IoU score of 0.64 and a Dice score of 0.79 were obtained thanks to the pre-training of the CNN on the secondary dataset and the creation of a novel deep label propagation model, followed by proper training on the primary dataset. To the best of our knowledge, these results are the best ever published in the segmentation of the IAC. The datasets is publicly available and algorithm is published as open-source software. On average, the CNN could produce a 3D annotation of the IAC in 6.33 s, compared to 87.3 s needed by the radiology technician to produce a bidimensional annotation. Conclusions: To resume, the following achievements have been reached. A new state of the art in terms of Dice score was achieved, overcoming the threshold commonly considered of 0.75 for the use in clinical practice. The CNN could fully automatically produce accurate three-dimensional segmentation of the IAC in a rapid setting, compared to the bidimensional annotations commonly used in the clinical practice and generated in a time-consuming manner. We introduced our innovative deep label propagation method to optimize the performance of the CNN in the segmentation of the IAC. For the first time in this field, the datasets and the source codes used were publicly released, granting reproducibility of the experiments and helping in the improvement of IAC segmentation.

Dynamic cone-beam CT reconstruction using spatial and temporal implicit neural representation learning (STINR)

Objective. Dynamic cone-beam CT (CBCT) imaging is highly desired in image-guided radiation therapy to provide volumetric images with high spatial and temporal resolutions to enable applications including tumor motion tracking/prediction and intra-delivery dose calculation/accumulation. However, dynamic CBCT reconstruction is a substantially challenging spatiotemporal inverse problem, due to the extremely limited projection sample available for each CBCT reconstruction (one projection for one CBCT volume). Approach. We developed a simultaneous spatial and temporal implicit neural representation (STINR) method for dynamic CBCT reconstruction. STINR mapped the unknown image and the evolution of its motion into spatial and temporal multi-layer perceptrons (MLPs), and iteratively optimized the neuron weightings of the MLPs via acquired projections to represent the dynamic CBCT series. In addition to the MLPs, we also introduced prior knowledge, in the form of principal component analysis (PCA)-based patient-specific motion models, to reduce the complexity of the temporal mapping to address the ill-conditioned dynamic CBCT reconstruction problem. We used the extended-cardiac-torso (XCAT) phantom and a patient 4D-CBCT dataset to simulate different lung motion scenarios to evaluate STINR. The scenarios contain motion variations including motion baseline shifts, motion amplitude/frequency variations, and motion non-periodicity. The XCAT scenarios also contain inter-scan anatomical variations including tumor shrinkage and tumor position change. Main results. STINR shows consistently higher image reconstruction and motion tracking accuracy than a traditional PCA-based method and a polynomial-fitting-based neural representation method. STINR tracks the lung target to an average center-of-mass error of 1–2 mm, with corresponding relative errors of reconstructed dynamic CBCTs around 10%. Significance. STINR offers a general framework allowing accurate dynamic CBCT reconstruction for image-guided radiotherapy. It is a one-shot learning method that does not rely on pre-training and is not susceptible to generalizability issues. It also allows natural super-resolution. It can be readily applied to other imaging modalities as well.

An Unsupervised Learning-Based Regional Deformable Model for Automated Multi-Organ Contour Propagation.

The aim of this study is to evaluate a regional deformable model based on a deep unsupervised learning model for automatic contour propagation in breast cone-beam computed tomography-guided adaptive radiation therapy. A deep unsupervised learning model was introduced to map breast's tumor bed, clinical target volume, heart, left lung, right lung, and spinal cord from planning computed tomography to cone-beam CT. To improve the traditional image registration method's performance, we used a regional deformable framework based on the narrow-band mapping, which can mitigate the effect of the image artifacts on the cone-beam CT. We retrospectively selected 373 anonymized cone-beam CT volumes from 111 patients with breast cancer. The cone-beam CTs are divided into three sets. 311 / 20 / 42 cone-beam CT images were used for training, validating, and testing. The manual contour was used as reference for the testing set. We compared the results between the reference and the model prediction for evaluating the performance. The mean Dice between manual reference segmentations and the model predicted segmentations for breast tumor bed, clinical target volume, heart, left lung, right lung, and spinal cord were 0.78 ± 0.09, 0.90 ± 0.03, 0.88 ± 0.04, 0.94 ± 0.03, 0.95 ± 0.02, and 0.77 ± 0.07, respectively. The results demonstrated a good agreement between the reference and the proposed contours. The proposed deep learning-based regional deformable model technique can automatically propagate contours for breast cancer adaptive radiotherapy. Deep learning in contour propagation was promising, but further investigation was warranted.