Critical anatomic region of nasopalatine canal based on tridimensional analysis: cone beam computed tomography.
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ABSTRACT:
SUBMITTER: Fernandez-Alonso A
PROVIDER: S-EPMC4526882 | biostudies-other | 2015
REPOSITORIES: biostudies-other
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Project description:BackgroundThe presence of middle mesial canals in mandibular molars are not commonly encountered in daily practice. However, locating and debriding such anatomical variation during the endodontic therapy is essential to avoid endodontic failure. The aim of this systematic review was to identify the overall prevalence of middle mesial canals (MMC) in mandibular molars using cone-beam computed tomography (CBCT), to assess the influence of different gender on the prevalence of MMCs in mandibular molars and to describe the MMC configurations.MethodsThe review protocol was registered in the PROSPERO database (CRD42021238523). The main electronic databases were searched until February 2022 for prevalence studies on root/canal anatomy in mandibular molars using CBCT imaging, in addition to hand-searched scientific articles in peer-reviewed journals and grey literature. The quality of the included studies was assessed using the Joanna Briggs Institute Critical Appraisal tool. Egger's and Begg's tests and Pearson's chi-square test were used for statistical analysis.ResultsFrom 523 records, 488 studies were excluded after screening the title and abstract. 35 relevant studies were included for full-text assessment and 28 studies were included in the meta-analysis. The overall prevalence of MMC out of the 19,256 teeth was 5.09% (95% CI: 2.894 to 7.784). The prevalence of MMC was 9.79% (95% CI: 4.296 to 17.224) in males and 13.96% (95% CI: 0.541 to 40.737) in females with a statistically significant difference of 4.16% (95% CI: 2.29 to 6.01). The most MMC configuration found in this review to be confluent with the mesiobuccal canal.ConclusionThe results showed an overall 5.09% prevalence of MMC in mandibular molars, with a significantly high prevalence in female subjects. Additionally, this canal was found to be confluent with the mesiobuccal canal.
| S-EPMC10724365 | biostudies-literature
Project description:Accurate localisation of mandibular canals in lower jaws is important in dental implantology, in which the implant position and dimensions are currently determined manually from 3D CT images by medical experts to avoid damaging the mandibular nerve inside the canal. Here we present a deep learning system for automatic localisation of the mandibular canals by applying a fully convolutional neural network segmentation on clinically diverse dataset of 637 cone beam CT volumes, with mandibular canals being coarsely annotated by radiologists, and using a dataset of 15 volumes with accurate voxel-level mandibular canal annotations for model evaluation. We show that our deep learning model, trained on the coarsely annotated volumes, localises mandibular canals of the voxel-level annotated set, highly accurately with the mean curve distance and average symmetric surface distance being 0.56 mm and 0.45 mm, respectively. These unparalleled accurate results highlight that deep learning integrated into dental implantology workflow could significantly reduce manual labour in mandibular canal annotations.
| S-EPMC7125134 | biostudies-literature
Project description:Background To evaluate the effects of dose to tumors and organs at risk (OARs) on inter-fractional anatomic changes. Methods We evaluated nine patients with cervical cancer treated with intensity-modulated radiotherapy (IMRT) (45 Gy in 25 fractions) using kV cone-beam computed tomography (CBCT) image guidance once or twice a week before treatment. For each patient, the original plan on the computed tomography (CT) image was copied to merged images, and then the fractional doses were calculated. Subsequently, deformable accumulated doses were obtained by summing the fractional absolute doses into a single dose in MIM Maestro software. The volume changes in the target and OARs were compared between the original CT and merged CBCT images, and the differences in the fractional and accumulated doses were also evaluated. Results Sixty-nine merged CBCT images were obtained and analyzed in this study. For the target areas, the volume changes in the clinical target volume (CTV) and planning target volume (PTV) reached −18.05% and −24.11% at most, respectively. The fractional D2% of the CTV and PTV was generally higher than the original plans, and the accumulated deviations were 2.27%±0.82% (P<0.01) and 2.42%±1.28% (P<0.01), respectively. The fractional D98% of the PTV was underdosed up to 18.28% for 78% of patients, and the accumulated deviations were −2.06% to −17.29% (P<0.05). For the OARs, the bladder volume changes were the most dramatic, reducing up to 93.60%. The fractional Dmean and D2cc of the bladder were generally higher than the original plans, and there were significant differences in their accumulated values (P<0.05). There was no obvious trend of rectal volume change with −69.65% to 74.20%. The rectum Dmean and D2cc of the accumulated were not significantly different from the planned dose (P>0.05). Conclusions For patients with cervical cancer, the changes in bladder and rectal volume were greater than in the target volume. Although the volume changes in the bladder and rectum had no significant effect on D98% of the CTV and PTV, they had a significant effect on their own D2cc and the D2% of the CTV and PTV. More attention should be paid to the volume changes in the bladder and rectum in clinical work.
| S-EPMC9843308 | biostudies-literature
Project description:Objectives:The aim of this study was to determine the location of the mandibular canal at the mental foramen region that is essential in order to prevent injuries to the inferior alveolar neurovascular bundle during mandibular surgical procedures. Material and Methods:The position of the mandibular canal was analysed using cone-beam computed tomography images from 300 Turkish patients, who were referred to Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Izmir Katip Celebi University for various complaints. The distances of the mandibular canal to the outer superior (D1), inferior (D2), buccal (D3), and lingual (D4) cortical margins were measured at 2 mm distal to the mental foramen. Results:There were 148 (49.3%) males and 152 (50.7%) females with ages ranging from 15 to 74 years (39.15 [SD 17.8]). D1 was significantly greater than D2 and D3 was significantly greater than D4 on both left and right sides. While the mean D1, D2, and D4 in males were significantly greater than in females on both left and right sides. There was no significant difference between D3 in males and females on both left and right sides. Conclusions:The results showed that the mandibular canal was vertically located nearer to the inferior cortical border and horizontally nearer to the lingual cortical border of the mandible at the mental foramen region. Knowledge of the distances of mandibular canal to the outer cortical margins at this region of the mandible will be helpful for surgical procedures.
| S-EPMC6683388 | biostudies-literature
Project description:Objectives:Retromolar canal (RC) is an anatomic structure, and due to increasing demand for surgical procedure in the retromolar area of the mandible, the identification of the retromolar canal has become an issue of clinical concern. It can innervate the third molar and some of the muscles around the posterior segment of the mandible, complicating surgical procedures in the retromolar area and root canal treatment of third molars. The aim of this study was to evaluate the incidence and anatomical properties of RC in a western Iranian population using cone-beam computed tomography (CBCT) images. Materials and Methods. Two hundred bilateral CBCT images were collected and screened in the three spatial planes for the presence of an RC. Anatomical properties and location of the RCs were assessed according to their course and distance from the surrounding structures. The relationship between the presence of RC and age, sex, side, and presence of second and third molars was also evaluated. Independent samples t-test, ANOVA, Tukey's post hoc test, paired t-test, ANOVA, Tukey's post hoc test, paired. Results:At least one RC was observed in 22% of the mandibles. Its bilateral incidence was 5.5%. Two major types of canals were detected, namely, type I, following a straight or curved course from the mandibular canal (MC) to the retromolar area (47.3%), and type II, coursing from the retromolar area to the radicular part of the third molar (52.7%). Regarding linear measurements, the mean RC diameter and the mean distance to the MC, second, and third molars were 0.68?±?0.31, 13.7?±?2.8, 15.3?±?3.0, and 7.3?±?2.3?mm, respectively. Conclusion:Based on the results of this study, RC was found in 22% of the cases; thus, it should be considered as a normal anatomical variation in the Iranian population rather than a rare finding.
| S-EPMC7085402 | biostudies-literature
Project description:Background and purposeRadiomics analysis extracts quantitative data (features) from medical images. These features could potentially reflect biological characteristics and act as imaging biomarkers within precision medicine. However, there is a lack of cross-comparison and validation of radiomics outputs which is paramount for clinical implementation. In this study, we compared radiomics outputs across two computed tomography (CT)-based preclinical scanners.Materials and methodsCone beam CT (CBCT) and µCT scans were acquired using different preclinical CT imaging platforms. The reproducibility of radiomics features on each scanner was assessed using a phantom across imaging energies (40 & 60 kVp) and segmentation volumes (44-238 mm3). Retrospective mouse scans were used to compare feature reliability across varying tissue densities (lung, heart, bone), scanners and after voxel size harmonisation. Reliable features had an intraclass correlation coefficient (ICC) > 0.8.ResultsFirst order and GLCM features were the most reliable on both scanners across different volumes. There was an inverse relationship between tissue density and feature reliability, with the highest number of features in lung (CBCT=580, µCT=734) and lowest in bone (CBCT=110, µCT=560). Comparable features for lung and heart tissues increased when voxel sizes were harmonised. We have identified tissue-specific preclinical radiomics signatures in mice for the lung (133), heart (35), and bone (15).ConclusionsPreclinical CBCT and µCT scans can be used for radiomics analysis to support the development of meaningful radiomics signatures. This study demonstrates the importance of standardisation and emphasises the need for multi-centre studies.
| S-EPMC11328005 | biostudies-literature
Project description:ObjectiveTo present and validate an open-source fully automated landmark placement (ALICBCT) tool for cone-beam computed tomography scans.Materials and methodsOne hundred and forty-three large and medium field of view cone-beam computed tomography (CBCT) were used to train and test a novel approach, called ALICBCT that reformulates landmark detection as a classification problem through a virtual agent placed inside volumetric images. The landmark agents were trained to navigate in a multi-scale volumetric space to reach the estimated landmark position. The agent movements decision relies on a combination of DenseNet feature network and fully connected layers. For each CBCT, 32 ground truth landmark positions were identified by 2 clinician experts. After validation of the 32 landmarks, new models were trained to identify a total of 119 landmarks that are commonly used in clinical studies for the quantification of changes in bone morphology and tooth position.ResultsOur method achieved a high accuracy with an average of 1.54 ± 0.87 mm error for the 32 landmark positions with rare failures, taking an average of 4.2 second computation time to identify each landmark in one large 3D-CBCT scan using a conventional GPU.ConclusionThe ALICBCT algorithm is a robust automatic identification tool that has been deployed for clinical and research use as an extension in the 3D Slicer platform allowing continuous updates for increased precision.
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Project description:Aim: To assess the association between demographic and clinical variables and the outcome of root-end surgery using digital periapical radiographs (PA) and cone-beam computed tomography (CBCT). Methodology: One hundred and fifty teeth that received endodontic microsurgery were clinically and radiographically examined (PA and CBCT scans) after 1 and 2 years. Two calibrated endodontists evaluated the radiographic healing based on a six-point outcome classification. The outcome was classified using both strict (healed) and loose (healing) criteria. The success rates were calculated, and several outcome prognostic factors were assessed. Results: One hundred and fifty teeth were assessed with both radiographic systems. When "loose" success criteria were applied using PA, 90% (n = 135) of teeth were assessed as having had a favourable outcome (90%; 95% CI: 85.2-94.8%), whereas 90.7% (n = 136) of teeth showed a successful outcome when assessed with CBCT (90.7%; 95% CI: 86.0-95.3%). When "strict" success criteria were applied, there was a statistically significant difference (p = 0.018) between the success rates of mineral trioxide aggregate (MTA) (63.7%) and Biodentine (95.5%). Conclusions: Within the limitations of this study, endodontic microsurgery showed a high success rate. Among all the demographic and clinical variables assessed, in the multiregression analysis, only the use of Biodentine was associated with a higher proportion of "complete" healings compared to MTA or Intermediate Restorative Material (IRM) when assessed using CBCT. MTA and Biodentine performed similarly when "incomplete" healings were regarded as successful outcomes.
| S-EPMC10971449 | biostudies-literature
Project description:Background and purposeCurrently, there is no robust indicator within the Cone-Beam Computed Tomography (CBCT) DICOM headers as to which anatomical region is present on the scan. This can be a predicament to CBCT-based algorithms trained on specific body regions, such as auto-segmentation and radiomics tools used in the radiotherapy workflow. We propose an anatomical region labeling (ARL) algorithm to classify CBCT scans into four distinct regions: head & neck, thoracic-abdominal, pelvis, and extremity.Materials and methodsAlgorithm training and testing was performed on 3,802 CBCT scans from 596 patients treated at our radiotherapy center. The ARL model, which consists of a convolutional neural network, makes use of a single CBCT coronal slice to output a probability of occurrence for each of the four classes. ARL was evaluated on the test dataset composed of 1,090 scans and compared to a support vector machine (SVM) model. ARL was also used to label CBCT treatment scans for 22 consecutive days as part of a proof-of-concept implementation. A validation study was performed on the first 100 unique patient scans to evaluate the functionality of the tool in the clinical setting.ResultsARL achieved an overall accuracy of 99.2% on the test dataset, outperforming the SVM (91.5% accuracy). Our validation study has shown strong agreement between the human annotations and ARL predictions, with accuracies of 99.0% for all four regions.ConclusionThe high classification accuracy demonstrated by ARL suggests that it may be employed as a pre-processing step for site-specific, CBCT-based radiotherapy tools.
| S-EPMC10020677 | biostudies-literature
Project description:Background and purposeAccurate and automated segmentation of targets and organs-at-risk (OARs) is crucial for the successful clinical application of online adaptive radiotherapy (ART). Current methods for cone-beam computed tomography (CBCT) auto-segmentation face challenges, resulting in segmentations often failing to reach clinical acceptability. Current approaches for CBCT auto-segmentation overlook the wealth of information available from initial planning and prior adaptive fractions that could enhance segmentation precision.Materials and methodsWe introduce a novel framework that incorporates data from a patient's initial plan and previous adaptive fractions, harnessing this additional temporal context to significantly refine the segmentation accuracy for the current fraction's CBCT images. We present LSTM-UNet, an innovative architecture that integrates Long Short-Term Memory (LSTM) units into the skip connections of the traditional U-Net framework to retain information from previous fractions. The models underwent initial pre-training with simulated data followed by fine-tuning on a clinical dataset.ResultsOur proposed model's segmentation predictions yield an average Dice similarity coefficient of 79% from 8 Head & Neck organs and targets, compared to 52% from a baseline model without prior knowledge and 78% from a baseline model with prior knowledge but no memory.ConclusionsOur proposed model excels beyond baseline segmentation frameworks by effectively utilizing information from prior fractions, thus reducing the effort of clinicians to revise the auto-segmentation results. Moreover, it works together with registration-based methods that offer better prior knowledge. Our model holds promise for integration into the online ART workflow, offering precise segmentation capabilities on synthetic CT images.
| S-EPMC11315102 | biostudies-literature