OBJECTIVE

The aim of this study was to qualitatively review the effects of genotoxicity and cytotoxicity on buccal mucosal epithelial cells after cone beam computed tomography (CBCT) exposure focusing on DNA damage and cell changes.

A literature search was carried out in PubMed, Wiley, Google Scholar, and Semantic Scholar for articles published in the last five years. In vivo studies that analyzed the DNA damage and cell changes on buccal mucosal epithelial cells, before and several days after CBCT exposure were included in this review. This review was prepared according to the PRISMA checklist for systematic review and the risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool.

A total of four studies were included in this review. The risk of bias analysis showed that all studies had generally good methodological quality. All the studies used buccal epithelial cells to analyze micronucleus (MN) as a parameter for DNA damage (genotoxicity), three of the studies also analyzed cytotoxicity using pyknotic nucleus and three studies analyzed karyolysis and karyorrhexis. All the studies consistently reported a significant increase in MN frequency, and cytotoxic effect were more evident before and 10-15 days after CBCT exposure.

This study demonstrated a significant impact on DNA and cell damage in oral mucosal cells following CBCT examination. The effect of ionizing radiation from CBCT has a more pronounced impact on cell damage than DNA damage.

Introduction: A negative computed tomography (CT) scan does not preclude an insulinoma, as small lesions frequently remain undetected on conventional imaging. This case highlights the indispensable role of endoscopic ultrasound (EUS)— not just for localizing occult tumors, but as a definitive, minimally invasive therapeutic alternative to high-risk surgical resection. Case: A 39-year-old female with underlying hypertension presented with a 5-month history of predominantly fasting hypoglycemia (glucose <3.0 mmol/L) and neuroglycopenic symptoms, fulfilling Whipple’s triad. A supervised 72-hour fast confirmed endogenous hyperinsulinemic hypoglycemia at 31 hours, with a nadir glucose of 1.4 mmol/L, insulin 116 pmol/L, and C-peptide 821 pmol/L. Notably, contrastenhanced CT of the pancreas was reported as normal. To overcome this, EUS was performed, successfully identifying a hidden 19 × 18 mm lesion in the head of the pancreas, intimately abutting the main pancreatic duct. Despite medical therapy with diazoxide and strict dietary modifications, her hypoglycemia remained refractory. Given the tumor’s proximity to the main pancreatic duct, surgical enucleation carried a prohibitively high risk of complications. Consequently, she underwent EUS-guided radiofrequency ablation (RFA). Immediate post-procedure outcomes demonstrated near-complete resolution of the hypoglycemic episodes. Diazoxide was subsequently stopped. Outpatient continuous glucose monitoring confirmed sustained normoglycemia and marked symptom resolution, with no procedure-related complications. Conclusion The absence of a pancreatic lesion on CT demands persistent clinical suspicion in cases of biochemically proven hypoglycemia. EUS remains paramount for detecting occult lesions missed by standard imaging. Importantly, EUSRFA serves as a highly effective, tissue-sparing alternative to surgical resection for insulinomas, especially when conventional surgery poses prohibitive anatomical risks.
Insulinoma ; Tomography, X-Ray Computed

Introduction: Insulinoma is a rare functioning pancreatic neuroendocrine tumor and the most common cause of endogenous hyperinsulinemic hypoglycemia. Although biochemical confirmation is usually straightforward, tumor localization may be difficult when lesions are small and not detected on conventional cross-sectional imaging. In such cases, endoscopic ultrasound (EUS) plays an important role in identifying occult lesions and facilitating definitive treatment. Case: A 52-year-old female was admitted in April 2025 with recurrent seizures secondary to hypoglycemia for 3 years, with increasing frequency over time. She fulfilled Whipple’s triad, with documented capillary glucose of 1.8 mmol/L during an episode and symptom resolution following glucose administration. A supervised prolonged fasting test confirmed endogenous hyperinsulinemic hypoglycemia, with plasma glucose 1.4 mmol/L, insulin 122 pmol/L, and C-peptide 1,010 pmol/L. Short Synacthen test demonstrated adequate adrenal reserve. Due to persistent hypoglycemia, she required high-dose diazoxide. Contrast-enhanced computed tomography abdomen did not reveal a pancreatic lesion but incidentally detected a right ovarian teratoma. She underwent total abdominal hysterectomy and bilateral salpingo-oophorectomy in June 2025, with histopathology confirming a mature cystic teratoma without malignancy. However, hypoglycemic episodes persisted. Further evaluation with EUS in July 2025 identified a highly vascular isoechoic 8 × 8 mm lesion in the pancreatic body. Fine-needle biopsy confirmed a well-differentiated neuroendocrine tumor (WHO grade 1) with Ki-67 index of 2%. She subsequently underwent EUSguided radiofrequency ablation in August 2025. Follow-up EUS in December 2025 showed post-ablation change, and her hypoglycemic episodes resolved completely, allowing diazoxide to be discontinued. Conclusion This case highlights the diagnostic challenge of occult insulinoma in the presence of negative conventional imaging. EUS was pivotal for tumor localization and tissue diagnosis, while EUS-guided radiofrequency ablation provided effective minimally invasive treatment in a carefully selected patient.
Insulinoma ; Tomography, X-Ray Computed

Synthetic CT (sCT) generated from CBCT has proven effective in artifact reduction and CT number correction, facilitating precise radiation dose calculation. However, the quality of different regions in sCT images is severely imbalanced, with soft tissue region exhibiting notably inferior quality compared to others. To address this imbalance, we proposed a Multi-Task Attention Network (MuTA-Net) based on VGG-16, specifically focusing the enhancement of image quality in soft tissue region of sCT. First, we introduced a multi-task learning strategy that divides the sCT generation task into three sub-tasks: global image generation, soft tissue region generation and bone region segmentation. This approach ensured the quality of overall sCT image while enhancing the network's focus on feature extraction and generation for soft tissues region. The result of bone region segmentation task guided the fusion of sub-tasks results. Then, we designed an attention module to further optimize feature extraction capabilities of the network. Finally, by employing a results fusion module, the results of three sub-tasks were integrated, generating a high-quality sCT image. Experimental results on head and neck CBCT demonstrated that the sCT images generated by the proposed MuTA-Net exhibited a 12.52% reduction in mean absolute error in soft tissue region, compared to the best performance among the three comparative methods, including ResNet, U-Net, and U-Net++. It can be seen that MuTA-Net is suitable for high-quality sCT image generation and has potential application value in the field of CBCT guided adaptive radiation therapy.
Cone-Beam Computed Tomography/methods* ; Humans ; Image Processing, Computer-Assisted/methods* ; Artifacts ; Algorithms ; Bone and Bones/diagnostic imaging* ; Neural Networks, Computer

This paper aims to propose a noninvasive radiotherapy patient positioning system based on structured light surface imaging, and evaluate its clinical feasibility. First, structured light sensors were used to obtain the panoramic point clouds during radiotherapy positioning in real time. The fusion of different point clouds and coordinate transformation were realized based on optical calibration and pose estimation, and the body surface was segmented referring to the preset region of interest (ROI). Then, the global-local registration of cross-source point cloud was achieved based on algorithms such as random sample consensus (RANSAC) and iterative closest point (ICP), to calculate 6 degrees of freedom (DoF) positioning deviation and provide guidance for the correction of couch shifts. The evaluation of the system was carried out based on a rigid adult phantom and volunteers' body, which included positioning error, correlation analysis, and receiver operating characteristic (ROC) analysis. Using Cone Beam CT (CBCT) as the gold standard, the maximum translation and rotation errors of this system were (1.5 ± 0.9) mm along Vrt direction (chest) and (0.7 ± 0.3) ° along Pitch direction (head and neck). The Pearson correlation coefficient between results of system outputs and CBCT verification distributed in an interval of [0.80, 0.84]. Results of ROC analysis showed that the translational and rotational AUC values were 0.82 and 0.85, respectively. In the 4D freedom accuracy test on the human body of volunteers, the maximum translation and rotation errors were (2.6 ± 1.1) mm (Vrt direction, chest and abdomen) and (0.8 ± 0.4)° (Rtn direction, chest and abdomen) respectively. In summary, the positioning system based on structured light body surface imaging proposed in this article can ensure positioning accuracy without surface markers and additional doses, and is feasible for clinical application.
Humans ; Patient Positioning/methods* ; Phantoms, Imaging ; Cone-Beam Computed Tomography ; Algorithms ; Radiotherapy, Image-Guided/methods* ; Radiotherapy Planning, Computer-Assisted/methods*

Among numerous medical imaging modalities, diffusion weighted imaging (DWI) is extremely sensitive to acute ischemic stroke lesions, especially small infarcts. However, magnetic resonance imaging is time-consuming and expensive, and it is also prone to interference from metal implants. Therefore, the aim of this study is to design a medical image synthesis method based on generative adversarial network, Stroke-p2pHD, for synthesizing DWI images from computed tomography (CT). Stroke-p2pHD consisted of a generator that effectively fused local image features and global context information (Global_to_Local) and a multi-scale discriminator (M ²Dis). Specifically, in the Global_to_Local generator, a fully convolutional Transformer (FCT) and a local attention module (LAM) were integrated to achieve the synthesis of detailed information such as textures and lesions in DWI images. In the M ²Dis discriminator, a multi-scale convolutional network was adopted to perform the discrimination function of the input images. Meanwhile, an optimization balance with the Global_to_Local generator was ensured and the consistency of features in each layer of the M ²Dis discriminator was constrained. In this study, the public Acute Ischemic Stroke Dataset (AISD) and the acute cerebral infarction dataset from Yantaishan Hospital were used to verify the performance of the Stroke-p2pHD model in synthesizing DWI based on CT. Compared with other methods, the Stroke-p2pHD model showed excellent quantitative results (mean-square error = 0.008, peak signal-to-noise ratio = 23.766, structural similarity = 0.743). At the same time, relevant experimental analyses such as computational efficiency verify that the Stroke-p2pHD model has great potential for clinical applications.
Humans ; Tomography, X-Ray Computed/methods* ; Diffusion Magnetic Resonance Imaging/methods* ; Cerebral Infarction/diagnostic imaging* ; Stroke/diagnostic imaging* ; Neural Networks, Computer ; Image Processing, Computer-Assisted/methods* ; Algorithms

In this study, we propose an automatic contour outlining method to measure the spatial resolution of homemade automatic tube current modulation (ATCM) phantom by outlining the edge contour of the phantom image, selecting the region of interest (ROI), and measuring the spatial resolution characteristics of computer tomography (CT) phantom image. Specifically, the method obtains a binarized image of the phantom outlined by an automated fast region convolutional neural network (AFRCNN) model, measures the edge spread function (ESF) of the CT phantom with different tube currents and layer thicknesses, and differentiates the ESF to obtain the line spread function (LSF). Finally, the values passing through the zeros are normalized by the Fourier transform to obtain the CT spatial resolution index (RI) for the automatic measurement of the modulation transfer function (MTF). In this study, this algorithm is compared with the algorithm that uses polymethylmethacrylate (PMMA) to measure the MTF of the phantom edges to verify the feasibility of this method, and the results show that the AFRCNN model not only improves the efficiency and accuracy of the phantom contour outlining, but also is able to obtain a more accurate spatial resolution value through automated segmentation. In summary, the algorithm proposed in this study is accurate in spatial resolution measurement of phantom images and has the potential to be widely used in real clinical CT images.
Phantoms, Imaging ; Tomography, X-Ray Computed/instrumentation* ; Algorithms ; Neural Networks, Computer ; Image Processing, Computer-Assisted/methods* ; Humans ; Polymethyl Methacrylate

Alveolar bone reconstruction simulation is an effective means for quantifying orthodontics, but currently, it is not possible to directly obtain human alveolar bone material models for simulation. This study introduces a prediction method for the equivalent shear modulus of three-dimensional random porous materials, integrating the first-order Ogden hyperelastic model to construct a computed tomography (CT) based porous hyperelastic Ogden model (CT-PHO) for human alveolar bone. Model parameters are derived by combining results from micro-CT, nanoindentation experiments, and uniaxial compression tests. Compared to previous predictive models, the CT-PHO model shows a lower root mean square error (RMSE) under all bone density conditions. Simulation results using the CT-PHO model parameters in uniaxial compression experiments demonstrate more accurate prediction of the mechanical behavior of alveolar bone under compression. Further prediction and validation with different individual human alveolar bone samples yield accurate results, confirming the generality of the CT-PHO model. The study suggests that the CT-PHO model proposed in this paper can estimate the material properties of human alveolar bone and may eventually be used for bone reconstruction simulations to guide clinical treatment.
Humans ; Tomography, X-Ray Computed/methods* ; Porosity ; Alveolar Process/physiology* ; Bone Density ; Computer Simulation ; Elasticity ; X-Ray Microtomography ; Stress, Mechanical ; Finite Element Analysis ; Models, Biological

To address the issues of difficulty in preserving anatomical structures, low realism of generated images, and loss of high-frequency image information in medical image cross-modal translation, this paper proposes a medical image cross-modal translation method based on diffusion generative adversarial networks. First, an unsupervised translation module is used to convert magnetic resonance imaging (MRI) into pseudo-computed tomography (CT) images. Subsequently, a nonlinear frequency decomposition module is used to extract high-frequency CT images. Finally, the pseudo-CT image is input into the forward process, while the high-frequency CT image as a conditional input is used to guide the reverse process to generate the final CT image. The proposed model is evaluated on the SynthRAD2023 dataset, which is used for CT image generation for radiotherapy planning. The generated brain CT images achieve a Fréchet Inception Distance (FID) score of 33.159 7, a structure similarity index measure (SSIM) of 89.84%, a peak signal-to-noise ratio (PSNR) of 35.596 5 dB, and a mean squared error (MSE) of 17.873 9. The generated pelvic CT images yield an FID score of 33.951 6, a structural similarity index of 91.30%, a PSNR of 34.870 7 dB, and an MSE of 17.465 8. Experimental results show that the proposed model generates highly realistic CT images while preserving anatomical accuracy as much as possible. The transformed CT images can be effectively used in radiotherapy planning, further enhancing diagnostic efficiency.
Humans ; Tomography, X-Ray Computed/methods* ; Magnetic Resonance Imaging/methods* ; Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer ; Brain/diagnostic imaging* ; Algorithms ; Radiotherapy Planning, Computer-Assisted ; Generative Adversarial Networks

In order to investigate the mechanical response of lumbar vertebrae during gait cycle in adolescents with idiopathic scoliosis (AIS), the present study was based on computed tomography (CT) data of AIS patients to construct model of the left support phase (ML) and model of the right support phase (MR), respectively. Firstly, material properties, boundary conditions and load loading were set to simulate the lumbar vertebra-pelvis model. Then, the difference of stress and displacement in the lumbar spine between ML and MR was compared based on the stress and displacement cloud map. The results showed that in ML, the lumbar stress was mostly distributed on the convex side, while in MR, it was mostly distributed on the concave side. The stress of the two types of stress mainly gathered near the vertebral arch plate, and the stress of the vertebral arch plate was transmitted to the vertebral body through the pedicle with the progress of gait. The average stress of the intervertebral tissue in MR was greater than that in ML, and the difference of stress on the convex and convex side was greater. The displacement of lumbar vertebrae in ML decreased gradually from L1 to L5. The opposite is true in MR. In conclusion, this study can accurately quantify the stress on the lumbar spine during gait, and may provide guidance for brace design and clinical decision making.
Humans ; Lumbar Vertebrae/diagnostic imaging* ; Scoliosis/diagnostic imaging* ; Adolescent ; Gait/physiology* ; Biomechanical Phenomena ; Tomography, X-Ray Computed ; Stress, Mechanical ; Female ; Male