[A dual-domain cone beam computed tomography reconstruction framework with improved differentiable domain transform for cone-angle artifact correction].

OBJECTIVE: We propose a dual-domain cone beam computed tomography (CBCT) reconstruction framework DualCBR-Net based on improved differentiable domain transform for cone-angle artifact correction. METHODS: The proposed CBCT dual-domain reconstruction framework DualCBR-Net consists of 3 individual modules: projection preprocessing, differentiable domain transform, and image post-processing. The projection preprocessing module first extends the original projection data in the row direction to ensure full coverage of the scanned object by X-ray. The differentiable domain transform introduces the FDK reconstruction and forward projection operators to complete the forward and gradient backpropagation processes, where the geometric parameters correspond to the extended data dimension to provide crucial prior information in the forward pass of the network and ensure the accuracy in the gradient backpropagation, thus enabling precise learning of cone-beam region data. The image post-processing module further fine-tunes the domain-transformed image to remove residual artifacts and noises. RESULTS: The results of validation experiments conducted on Mayo's public chest dataset showed that the proposed DualCBR-Net framework was superior to other comparison methods in terms of artifact removal and structural detail preservation. Compared with the latest methods, the DualCBR-Net framework improved the PSNR and SSIM by 0.6479 and 0.0074, respectively. CONCLUSION: The proposed DualCBR-Net framework for cone-angle artifact correction allows effective joint training of the CBCT dual-domain network and is especially effective for large cone-angle region.

[A deep blur learning-based motion artifact reduction algorithm for dental cone-beam computed tomography images].

OBJECTIVE: We propose a motion artifact correction algorithm (DMBL) for reducing motion artifacts in reconstructed dental cone-beam computed tomography (CBCT) images based on deep blur learning. METHODS: A blur encoder was used to extract motion-related degradation features to model the degradation process caused by motion, and the obtained motion degradation features were imported in the artifact correction module for artifact removal. The artifact correction module adopts a joint learning framework for image blur removal and image blur simulation for treatment of spatially varying and random motion patterns. Comparative experiments were conducted to verify the effectiveness of the proposed method using both simulated motion data sets and clinical data sets. RESULTS: The experimental results with the simulated dataset showed that compared with the existing methods, the PSNR of the proposed method increased by 2.88%, the SSIM increased by 0.89%, and the RMSE decreased by 10.58%. The results with the clinical dataset showed that the proposed method achieved the highest expert level with a subjective image quality score of 4.417 (in a 5-point scale), significantly higher than those of the comparison methods. CONCLUSION: The proposed DMBL algorithm with a deep blur joint learning network structure can effectively reduce motion artifacts in dental CBCT images and achieve high-quality image restoration.

PIEZO1 Promotes Odontoblast-Mediated Reactionary Dentinogenesis via SEMA3A.

Located at the interface of the dentin-pulp complex, the odontoblasts are specialized cells responsible for dentin synthesis and nociceptive signal detection in response to external stimuli. Recent studies have shown that the mechanosensitive ion channel PIEZO1 is involved in bone formation and remodeling through the influx of calcium ions, and it is abundantly expressed in odontoblasts. However, the specific role of PIEZO1 in reactionary dentinogenesis and the underlying mechanisms remain elusive. In this study, we found intense PIEZO1 expression in the plasma membrane and cytoplasm of odontoblasts in healthy human third molars, mouse mandibular molars, and human odontoblast-like cells (hOBLCs). In hOBLCs, PIEZO1 positively regulated DSPP, DMP1, and COL1A1 expression through the Ca2+/PI3K-Akt/SEMA3A signaling pathway. In addition, exogenous SEMA3A supplementation effectively reversed reduced mineralization capacity in PIEZO1-knockdown hOBLCs. In vivo, Piezo1 expression peaked at day 7 and returned to baseline at day 21 in a wild-type mice dentin injury model, with Sema3a presenting a similar expression pattern. To investigate the specific role of PIEZO1 in odontoblast-mediated reactionary dentinogenesis, mice with a conditional knockout of Piezo1 in odontoblasts were generated, and no significant differences in teeth phenotypes were observed between the control and conditional knockout (cKO) mice. Nevertheless, cKO mice exhibited reduced reactionary dentin formation and decreased Sema3a and Dsp positive staining after dentin injury, indicating impaired dental pulp repair by odontoblasts. In summary, these findings suggest that PIEZO1 enhances the mineralization capacity of hOBLCs in vitro via the Ca2+/PI3K-Akt/SEMA3A signaling pathway and contributes to reactionary dentinogenesis in vivo.

[Reconstruction from CT truncated data based on dual-domain transformer coupled feature learning].

OBJECTIVE: To propose a CT truncated data reconstruction model (DDTrans) based on projection and image dualdomain Transformer coupled feature learning for reducing truncation artifacts and image structure distortion caused by insufficient field of view (FOV) in CT scanning. METHODS: Transformer was adopted to build projection domain and image domain restoration models, and the long-range dependency modeling capability of the Transformer attention module was used to capture global structural features to restore the projection data information and enhance the reconstructed images. We constructed a differentiable Radon back-projection operator layer between the projection domain and image domain networks to enable end-to-end training of DDTrans. Projection consistency loss was introduced to constrain the image forwardprojection results to further improve the accuracy of image reconstruction. RESULTS: The experimental results with Mayo simulation data showed that for both partial truncation and interior scanning data, the proposed DDTrans method showed better performance than the comparison algorithms in removing truncation artifacts at the edges and restoring the external information of the FOV. CONCLUSION: The DDTrans method can effectively remove CT truncation artifacts to ensure accurate reconstruction of the data within the FOV and achieve approximate reconstruction of data outside the FOV.

[A low- dose CT reconstruction algorithm across different scanners based on federated feature learning].

OBJECTIVE: To propose a low-dose CT reconstruction algorithm across different scanners based on federated feature learning (FedCT) to improve the generalization of deep learning models for multiple CT scanners and protect data privacy. METHODS: In the proposed FedCT framework, each client is assigned an inverse Radon transform-based reconstruction model to serve as a local network model that participates in federated learning. A projection- domain specific learning strategy is adopted to preserve the geometry specificity in the local projection domain. Federated feature learning is introduced in the model, which utilizes conditional parameters to mark the local data and feed the conditional parameters into the network for encoding to enhance the generalization of the model in the image domain. RESULTS: In the cross-client, multi-scanner, and multi-protocol low-dose CT reconstruction experiments, FedCT achieved the highest PSNR (+2.8048, +2.7301, and +2.7263 compared to the second best federated learning method), the highest SSIM (+0.0009, +0.0165, and +0.0131 in the same comparison), and the lowest RMSE (- 0.6687, - 1.5956, and - 0.9962). In the ablation experiment, compared with the general federated learning strategy, the model with projection-specific learning strategy showed an average improvement by 1.18 on Q1 of the PSNR and an average decrease by 1.36 on Q3 of the RMSE on the test set. The introduction of federated feature learning in FedCT further improved the Q1 of the PSNR on the test set by 3.56 and reduced the Q3 of the RMSE by 1.80. CONCLUSION: FedCT provides an effective solution for collaborative construction of CT reconstruction models, which can enhance model generalization and further improve the reconstruction performance on global data while protecting data privacy.

[Research progress in associations between dental caries and systemic diseases].

Dental caries is a bacteria-mediated, multifactorial, chronic progressive disease that results in the phasic demineralization and remineralization of dental hard tissues. In recent years, amounts of studies have focused on the association between dental caries and systemic diseases. This paper reviews the researches about associations between caries and systemic diseases. An electronic search was conducted in PubMed and Web of Science for articles published from 2003 to 2022 in the English language. Studies were included in the following ten categories of systemic diseases: cardiovascular diseases, metabolic disorders, respiratory diseases, autoimmune rheumatic diseases, neurologic diseases, gastrointestinal diseases, kidney diseases, skin diseases, iron deficiency anaemia and tumors. This review discusses the relationship between dental caries and systemic diseases, as well as the potentially involved mechanisms, providing new ideas for disease prevention, diagnosis, and treatment strategies for dentists and other clinicians.

Clinically Oriented CBCT Periapical Lesion Evaluation via 3D CNN Algorithm.

Apical periodontitis (AP) is one of the most prevalent disorders in dentistry. However, it can be underdiagnosed in asymptomatic patients. In addition, the perioperative evaluation of 3-dimensional (3D) lesion volume is of great clinical relevance, but the required slice-by-slice manual delineation method is time- and labor-intensive. Here, for quickly and accurately detecting and segmenting periapical lesions (PALs) associated with AP on cone beam computed tomography (CBCT) images, we proposed and geographically validated a novel 3D deep convolutional neural network algorithm, named PAL-Net. On the internal 5-fold cross-validation set, our PAL-Net achieved an area under the receiver operating characteristic curve (AUC) of 0.98. The algorithm also improved the diagnostic performance of dentists with varying levels of experience, as evidenced by their enhanced average AUC values (junior dentists: 0.89-0.94; senior dentists: 0.91-0.93), and significantly reduced the diagnostic time (junior dentists: 69.3 min faster; senior dentists: 32.4 min faster). Moreover, our PAL-Net achieved an average Dice similarity coefficient over 0.87 (0.85-0.88), which is superior or comparable to that of other existing state-of-the-art PAL segmentation algorithms. Furthermore, we validated the generalizability of the PAL-Net system using multiple external data sets from Central, East, and North China, showing that our PAL-Net has strong robustness. Our PAL-Net can help improve the diagnostic performance and speed of dentists working from CBCT images, provide clinically relevant volume information to dentists, and can potentially be applied in dental clinics, especially without expert-level dentists or radiologists.

Persistent clinical symptoms and their association with CM syndromes in post-COVID-19 rehabilitation patients in Hong Kong.

Background: Heterogeneous clinical conditions were observed in individuals who had recovered from COVID-19 and some symptoms were found to persist for an extended period post-COVID. Given the non-specific nature of the symptoms, Chinese medicine (CM) is advantageous in providing holistic medical assessment for individuals experiencing persisting problems. Chinese medicine is a type of treatment that involves prescribing regimens based on CM Syndromes diagnosed by CM practitioners. However, inadequate research on CM elements behind the practice has faced scrutiny. Methods: This study analysed 1058 CM medical records from 150 post-COVID-19 individuals via a semi-text-mining approach. A logistic model with MCMCglmm was then utilised to analyse the associations between the indicated factors and identified conditions. Calculations were performed using R Studio and related libraries. Results: With the semi-text-mining approach, three common CM Syndromes (Qi and Yin Deficiency, Lung and Spleen Deficiency, Qi Deficiency of both Spleen and Lung) and nine clinical conditions (fatigue, poor sleep, dry mouth, shortness of breath, cough, headache, tiredness, sweating, coughing phlegm) were identified in the CM clinical records. Analysis via MCMCglmm revealed that the occurrence of persisting clinical conditions was significantly associated with female gender, existing chronic conditions (hypertension, high cholesterol, and diabetes mellitus), and the three persisting CM Syndromes. The current study triangulated the findings from our previous observational study, further showing that patients with certain post-COVID CM Syndromes had significantly increased log-odds of having persisting clinical conditions. Furthermore, this study elucidated that the presence of chronic conditions in the patients would also significantly increase the log-odds of having persistent post-COVID clinical conditions. Conclusion: This study provided insights on mining text-based CM clinical records to identify persistent post-COVID clinical conditions and the factors associated with their occurrence. Future studies could examine the integration of integrating exercise modules, such as health qigong Liuzijue, into multidisciplinary rehabilitation programmes.

[Recognition on dentin dysplasia type â ¡].

Dentin dysplasia type Ⅱ (DD-Ⅱ) is a subtype of hereditary dentin disorders. The dentin sialophosphoprotein (DSPP) gene has been revealed to be the causative gene, whose mutations could affect the normal tooth development process. The lesions involve both deciduous and permanent dentition, mainly manifested as tooth discoloration, attrition and even the subsequent malocclusion. If not treated in time, it will significantly affect the physical and psychological health of patients. The disease is difficult to be diagnosed in clinic accurately as its low incidence and hidden manifestations. The present article aims to discuss the clinical and radiographic characteristics, diagnosis, treatment of DD-Ⅱ, in order to improve the overall understanding on DD-Ⅱ for clinicians.

[Two novel and de novo KMT2D mutations on the same allele cause Kabuki syndrome].

Objective: To screen the candidate genes in a patient with Kabuki syndrome (KS), providing basis for genetic counseling, prenatal screening, prenatal diagnosis and facilitating early treatment. Methods: This study included a 16-year-old female KS patient born of non-consanguineous Chinese parents who presented to Department of Orthognathic & Cleft Lip and Palate Plastic Surgery, School and Hospital of Stomatology, Wuhan University. Genomic DNA was extracted from the peripheral blood of the subjects and analyzed by whole-exome sequencing (WES). Sanger sequencing was performed to validate the mutation in the candidate gene. The conformational and physicochemical changes of the mutant were analyzed by Alphafold2, Antheprot and DOG.2.0.1, respectively. Distribution of KMT2D mutations in patients with KS was analyzed based on the Human Gene Mutation Database Results: The proband manifested a typical KS facial gestalt, congenital cleft palate, fifth finger deformity, hypodontia, renal hypoplasia and hydronephrosis. Two de novo mutations c.[1166A>C; 1167dupC] (NM_003482) in cis on the same allele in the KMT2D gene were identified by WES and confirmed by allele-specific PCR. Bioinformatics analysis showed that three more α-helixes were added, and a (ß-) turn and a (ß-) sheet were reduced in KMT2D p. Y389S, p.V390Rfs*26 compared with the wild type. Meanwhile, the interceptive mutant-KMT2D protein p.V390Rfs*26 lost all four domains (FYRN domain, FYRC domain, SET domain, and PostSET domain), which may cause functional disabilities. Conclusions: Our study is the first to identify two novel and de novo KMT2D mutations in cis on the same allele in a KS patient and extends the KMT2D mutation spectrum of KS, providing evidence for genetic susceptibility counseling, prenatal screening and diagnosis, and early treatment of KS.

[A semi-supervised network-based tissue-aware contrast enhancement method for CT images].

OBJECTIVE: To propose a tissue- aware contrast enhancement network (T- ACEnet) for CT image enhancement and validate its accuracy in CT image organ segmentation tasks. METHODS: The original CT images were mapped to generate low dynamic grayscale images with lung and soft tissue window contrasts, and the supervised sub-network learned to recognize the optimal window width and level setting of the lung and abdominal soft tissues via the lung mask. The self-supervised sub-network then used the extreme value suppression loss function to preserve more organ edge structure information. The images generated by the T-ACEnet were fed into the segmentation network to segment multiple abdominal organs. RESULTS: The images obtained by T-ACEnet were capable of providing more window setting information in a single image, which allowed the physicians to conduct preliminary screening of the lesions. Compared with the suboptimal methods, T-ACE images achieved improvements by 0.51, 0.26, 0.10, and 14.14 in SSIM, QABF, VIFF, and PSNR metrics, respectively, with a reduced MSE by an order of magnitude. When T-ACE images were used as input for segmentation networks, the organ segmentation accuracy could be effectively improved without changing the model as compared with the original CT images. All the 5 segmentation quantitative indices were improved, with the maximum improvement of 4.16%. CONCLUSION: The T-ACEnet can perceptually improve the contrast of organ tissues and provide more comprehensive and continuous diagnostic information, and the T-ACE images generated using this method can significantly improve the performance of organ segmentation tasks.

[A semi-supervised material quantitative intelligent imaging algorithm for spectral CT based on prior information perception learning].

OBJECTIVE: To propose a semi-supervised material quantitative intelligent imaging algorithm based on prior information perception learning (SLMD-Net) to improve the quality and precision of spectral CT imaging. METHODS: The algorithm includes a supervised and a self- supervised submodule. In the supervised submodule, the mapping relationship between low and high signal-to-noise ratio (SNR) data was constructed through mean square error loss function learning based on a small labeled dataset. In the self- supervised sub-module, an image recovery model was utilized to construct the loss function incorporating the prior information from a large unlabeled low SNR basic material image dataset, and the total variation (TV) model was used to to characterize the prior information of the images. The two submodules were combined to form the SLMD-Net method, and pre-clinical simulation data were used to validate the feasibility and effectiveness of the algorithm. RESULTS: Compared with the traditional model-driven quantitative imaging methods (FBP-DI, PWLS-PCG, and E3DTV), data-driven supervised-learning-based quantitative imaging methods (SUMD-Net and BFCNN), a material quantitative imaging method based on unsupervised learning (UNTV-Net) and semi-supervised learning-based cycle consistent generative adversarial network (Semi-CycleGAN), the proposed SLMD-Net method had better performance in both visual and quantitative assessments. For quantitative imaging of water and bone materials, the SLMD-Net method had the highest PSNR index (31.82 and 29.06), the highest FSIM index (0.95 and 0.90), and the lowest RMSE index (0.03 and 0.02), respectively) and achieved significantly higher image quality scores than the other 7 material decomposition methods (P < 0.05). The material quantitative imaging performance of SLMD-Net was close to that of the supervised network SUMD-Net trained with labeled data with a doubled size. CONCLUSIONS: A small labeled dataset and a large unlabeled low SNR material image dataset can be fully used to suppress noise amplification and artifacts in basic material decomposition in spectral CT and reduce the dependence on labeled data-driven network, which considers more realistic scenario in clinics.

[Effects of circadian rhythm on the development of dental hard tissues].

Circadian rhythm is regulated by circadian clock, which is formed by the body response to external cyclic stimuli through the endogenous circadian clock. Circadian rhythm disturbance is closely related to the risks of a variety of diseases, and its impact on oral health cannot be ignored. Exploring the relationship and related molecular mechanism between circadian rhythm and dental hard tissues development are helpful to deeply understand the pathogenesis of developmental defects on these tissues, which could provide a theoretical basis for prevention and treatment on disorders of dental hard tissues. In order to provide guidance for the disease prevention and treatment, based on the summarization of current research progress, this paper focuses on the involvement of biorhythm in the development of tooth hard tissues as well as the disturbance of circadian rhythm on the formation of enamel and dentin, and analyzes the related regulating mechanism of circadian rhythm and genes during the development of tooth hard tissues.

[An adaptive CT metal artifact reduction algorithm that combines projection interpolation and physical correction].

OBJECTIVE: To propose an adaptive weighted CT metal artifact reduce algorithm that combines projection interpolation and physical correction. METHODS: A normalized metal projection interpolation algorithm was used to obtain the initial corrected projection data. A metal physical correction model was then introduced to obtain the physically corrected projection data. To verify the effectiveness of the method, we conducted experiments using simulation data and clinical data. For the simulation data, the quantitative indicators PSNR and SSIM were used for evaluation, while for the clinical data, the resultant images were evaluated by imaging experts to compare the artifact-reducing performance of different methods. RESULTS: For the simulation data, the proposed method improved the PSNR value by at least 0.2 dB and resulted in the highest SSIM value among the methods for comparison. The experiment with the clinical data showed that the imaging experts gave the highest scores of 3.616±0.338 (in a 5-point scale) to the images processed using the proposed method, which had significant better artifact-reducing performance than the other methods (P < 0.001). CONCLUSION: The metal artifact reduction algorithm proposed herein can effectively reduce metal artifacts while preserving the tissue structure information and reducing the generation of new artifacts.

[Low-dose helical CT projection data restoration using noise estimation].

OBJECTIVE: To build a helical CT projection data restoration model at random low-dose levels. METHODS: We used a noise estimation module to achieve noise estimation and obtained a low-dose projection noise variance map, which was used to guide projection data recovery by the projection data restoration module. A filtering back-projection algorithm (FBP) was finally used to reconstruct the images. The 3D wavelet group residual dense network (3DWGRDN) was adopted to build the network architecture of the noise estimation and projection data restoration module using asymmetric loss and total variational regularization. For validation of the model, 1/10 and 1/15 of normal dose helical CT images were restored using the proposed model and 3 other restoration models (IRLNet, REDCNN and MWResNet), and the results were visually and quantitatively compared. RESULTS: Quantitative comparisons of the restored images showed that the proposed helical CT projection data restoration model increased the structural similarity index by 5.79% to 17.46% compared with the other restoration algorithms (P < 0.05). The image quality scores of the proposed method rated by clinical radiologists ranged from 7.19% to 17.38%, significantly higher than the other restoration algorithms (P < 0.05). CONCLUSION: The proposed method can effectively suppress noises and reduce artifacts in the projection data at different low-dose levels while preserving the integrity of the edges and fine details of the reconstructed CT images.

[A nonlocal spectral similarity-induced material decomposition method for noise reduction of dual-energy CT images].

OBJECTIVE: To propose a nonlocal spectral similarity-induced material decomposition network (NSSD-Net) to reduce the correlation noise in the low-dose spectral CT decomposed images. METHODS: We first built a model-driven iterative decomposition model for dual-energy CT, optimized the objective function solving process using the iterative shrinking threshold algorithm (ISTA), and cast the ISTA decomposition model into the deep learning network. We then developed a novel cost function based on the nonlocal spectral similarity to constrain the training process. To validate the decomposition performance, we established a material decomposition dataset by real patient dual-energy CT data. The NSSD-Net was compared with two traditional model-driven material decomposition methods, one data-based material decomposition method and one data-model coupling-driven material decomposition supervised learning method. RESULTS: The quantitative results showed that compared with the two traditional methods, the NSSD-Net method obtained the highest PNSR values (31.383 and 31.444) and SSIM values (0.970 and 0.963) and the lowest RMSE values (2.901 and 1.633). Compared with the datamodel coupling-driven supervised decomposition method, the NSSD-Net method obtained the highest SSIM values on water and bone decomposed results. The results of subjective image quality assessment by clinical experts showed that the NSSD-Net achieved the highest image quality assessment scores on water and bone basis material (8.625 and 8.250), showing significant differences from the other 4 decomposition methods (P < 0.001). CONCLUSION: The proposed method can achieve high-precision material decomposition and avoid training data quality issues and model unexplainable issues.

[Performance of low-dose CT image reconstruction for detecting intracerebral hemorrhage: selection of dose, algorithms and their combinations].

OBJECTIVE: To investigate the performance of different low-dose CT image reconstruction algorithms for detecting intracerebral hemorrhage. METHODS: Low-dose CT imaging simulation was performed on CT images of intracerebral hemorrhage at 30%, 25% and 20% of normal dose level (defined as 100% dose). Seven algorithms were tested to reconstruct low-dose CT images for noise suppression, including filtered back projection algorithm (FBP), penalized weighted least squares-total variation (PWLS-TV), non-local mean filter (NLM), block matching 3D (BM3D), residual encoding-decoding convolutional neural network (REDCNN), the FBP convolutional neural network (FBPConvNet) and image restoration iterative residual convolutional network (IRLNet). A deep learning-based model (CNN-LSTM) was used to detect intracerebral hemorrhage on normal dose CT images and low-dose CT images reconstructed using the 7 algorithms. The performance of different reconstruction algorithms for detecting intracerebral hemorrhage was evaluated by comparing the results between normal dose CT images and low-dose CT images. RESULTS: At different dose levels, the low-dose CT images reconstructed by FBP had accuracies of detecting intracerebral hemorrhage of 82.21%, 74.61% and 65.55% at 30%, 25% and 20% dose levels, respectively. At the same dose level (30% dose), the images reconstructed by FBP, PWLS-TV, NLM, BM3D, REDCNN, FBPConvNet and IRLNet algorithms had accuracies for detecting intracerebral hemorrhage of 82.21%, 86.80%, 89.37%, 81.43%, 90.05%, 90.72% and 93.51%, respectively. The images reconstructed by IRLNet at 30%, 25% and 20% dose levels had accuracies for detecting intracerebral hemorrhage of 93.51%, 93.51% and 93.06%, respectively. CONCLUSION: The performance of reconstructed low-dose CT images for detecting intracerebral hemorrhage is significantly affected by both dose and reconstruction algorithms. In clinical practice, choosing appropriate dose level and reconstruction algorithm can greatly reduce the radiation dose and ensure the detection performance of CT imaging for intracerebral hemorrhage.

[An in vitro model study of computer-guided endodontic microsurgery].

Objective: To assess and compare the accuracies and operating time of endodontic microsurgery performed by operators with different levels of experience in endodontics using computer-guided techniques including dynamic and static navigations in a surgical simulation model. Methods: Six pairs of three dimensional (3D)-printed models of upper and lower jaws were set up on dental manikins. A total of 120 teeth (10 teeth each jaw) were included in the models. Microsurgeries of osteotomy and root-resection were performed on the models by two operators with different experience, namely novices and experts, under of free hand (FH)(n=20), dynamic navigation (DN)(n=20), and static navigation (SN)(n=20) conditions, respectively. The duration of each operation was recorded. Cone-beam CT was taken for 3D-printed models before and after the operation. The path of preoperative surgery planning was simulated. The linear deviations at the entry and the end point and the angular deviation of the access path between the simulated and the actual operation were compared by the software. Results: Significant difference of the entry deviation was observed between the novices and the experts in the FH group [(1.44±0.49) and (1.02±0.58) mm] (q=4.67, P=0.020). There were no significant differences between the novices and the experts in the end point and angular deviations (P>0.05). For the novices, the entry deviations in both DN and SN groups [(0.76±0.32) and (0.66±0.20) mm] were significantly lower than those in FH group (q=7.58, P<0.001; q=8.66, P<0.001). The angular deviations in the abovementioned two groups (5.0°±3.5°, 3.9°±2.1°) were significantly lower than that in FH group (10.9°±6.1°) (q=7.38, P<0.001; q=8.70, P<0.001). For the experts, significant differences were found only in the angular deviations among DN, SN and FH groups (3.6°±1.9°, 3.2°±1.7° and 8.2°±3.9°) (q=5.74, P=0.001; q=6.29, P<0.001). The operation durations were significantly shortened for both the novices [(4.80±2.15), (1.09±0.48) min] (q=14.60, P<0.001; q=20.10, P<0.001) and the experts [(3.40±1.96),(1.02±0.34) min] (q=5.86, P<0.001; q=9.37, P<0.001) by using DN and SN techniques. Regarding the differences between tooth types, in FH group, the operating time on the anterior teeth was significantly shorter than that on the posterior teeth (q=8.14, P<0.001; q=5.20, P=0.007), while in DN and SN groups, there were no significant differences in the operating time between two tooth types (P>0.05). No significant differences were discovered in the accuracies on the anterior and posterior teeth among three techniques or between two kinds of operators (P>0.05). Conclusions: Dynamic and static navigation techniques could assist the clinicians, especially the novices, to improve the accuracies and shorten the operating time of osteotomy and root resection microsurgeries.