Evaluation of Positive Predictive Value for Digital Panoramic Radiography in Comparison to Ultrasound in the Diagnosis of Calcified Carotid Atheroma

Aim: Detection of calcified carotid atheroma (CCA) has an important role in reducing the incidence of Cerebro Vascular Accident (CVA). The aim of this study was to evaluate efficacy of panoramic digital radiography in detecting atherosclerosis. Methods: It is descriptive-analytical diagnostic study. The people (22 to 62 years old) were referred to a radiology clinic to perform panoramic radiography for diagnosis of CCA. Individuals who were suspected were introduced to the radiology department of dental school to undergo ultrasound evaluation to CCA. For the 41 patients (55 sides), ultrasound was performed. For data analysis, the Chi-square and Fisher's exact test were used. Results: The prevalence of CCA was 2.43%. The PPV of digital panoramic was 45.5%. There was no significant relationship between age (P = 0.14) and sex (P = 0.539) and PPV of digital panoramic. The PPV of digital panoramic was significantly associated with hypertension (P = 0.032). Conclusion: It seems that panoramic can be used to screen patients with a history of hypertension for atherosclerosis.

Relevance and Compliance of Requests for Dental Panoramic Radiography Examination at Yaounde (Cameroon)

Background: Research studies made in Cameroon on compliance and relevance in the various areas of imaging, show the failure to comply with administrative and clinical criteria. These research studies led to recommendations meant for result amelioration. However, the orthopantomogram (OPT) field remains less studied in that regard in Cameroon. Aim: Evaluate the relevance and compliance of examination requests for panoramic dental radiography. Setting and Design: It is a descriptive cross-sectional study with a consecutive sample of dental panoramic requests identified during the study period in 4 radiology centres of Yaoundé. Material and Methods: Variables studied here were the validity criteria of imaging examination. Request: Five of which are of administrative order (date of the request, requesting department, patient’s identity, patient’s age, applicant’s identity) and three of clinical order (anatomic region, reason for the examination and the purpose of the examination). Statistical Analysis: Chi-squared test was used with confidence interval of 95%. Results: Patient’s identity (name) was the criteria carrying the highest information (98.9%), followed by the name of the applicant physician (91.6%). Out of 179 requests analysed, 8.6% had complete information. 46.4% of requests had no indications. Dental surgeon was the top prescriber with a total of 112 (62.6%). Conclusion: The quality of dental panoramic requests was not optimal in Yaounde, with lack of precision mostly noted at the level of clinical criteria.

Single Tooth Segmentation on Panoramic X-Rays Using End-to-End Deep Neural Networks

In dentistry, panoramic X-ray images are extensively used by dentists for tooth structure analysis and disease diagnosis. However, the manual analysis of these images is time-consuming and prone to misdiagnosis or overlooked. While deep learning techniques have been employed to segment teeth in panoramic X-ray images, accurate segmentation of individual teeth remains an underexplored area. In this study, we propose an end-to-end deep learning method that effectively addresses this challenge by employing an improved combinatorial loss function to separate the boundaries of adjacent teeth, enabling precise segmentation of individual teeth in panoramic X-ray images. We validate the feasibility of our approach using a challenging dataset. By training our segmentation network on 115 panoramic X-ray images, we achieve an intersection over union (IoU) of 86.56% for tooth segmentation and an accuracy of 65.52% in tooth counting on 87 test set images. Experimental results demonstrate the significant improvement of our proposed method in single tooth segmentation compared to existing methods.

Intelligent extraction of road cracks based on vehicle laser point cloud and panoramic sequence images

In light of the limited efficacy of conventional methods for identifying pavement cracks and the absence of comprehensive depth and location data in two-dimensional photographs,this study presents an intelligent strategy for extracting road cracks.This methodology involves the integration of laser point cloud data obtained from a vehicle-mounted system and a panoramic sequence of images.The study employs a vehicle-mounted LiDAR measurement system to acquire laser point cloud and panoramic sequence image data simultaneously.A convolutional neural network is utilized to extract cracks from the panoramic sequence image.The extracted sequence image is then aligned with the laser point cloud,enabling the assignment of RGB information to the vehicle-mounted three dimensional(3D)point cloud and location information to the two dimensional(2D)panoramic image.Additionally,a threshold value is set based on the crack elevation change to extract the aligned roadway point cloud.The three-dimensional data pertaining to the cracks can be acquired.The experimental findings demonstrate that the use of convolutional neural networks has yielded noteworthy outcomes in the extraction of road cracks.The utilization of point cloud and image alignment techniques enables the extraction of precise location data pertaining to road cracks.This approach exhibits superior accuracy when compared to conventional methods.Moreover,it facilitates rapid and accurate identification and localization of road cracks,thereby playing a crucial role in ensuring road maintenance and traffic safety.Consequently,this technique finds extensive application in the domains of intelligent transportation and urbanization development.The technology exhibits significant promise for use in the domains of intelligent transportation and city development.

Radiography Image Classification Using Deep Convolutional Neural Networks

Research has shown that chest radiography images of patients with different diseases, such as pneumonia, COVID-19, SARS, pneumothorax, etc., all exhibit some form of abnormality. Several deep learning techniques can be used to identify each of these anomalies in the chest x-ray images. Convolutional neural networks (CNNs) have shown great success in the fields of image recognition and image classification since there are numerous large-scale annotated image datasets available. The classification of medical images, particularly radiographic images, remains one of the biggest hurdles in medical diagnosis because of the restricted availability of annotated medical images. However, such difficulty can be solved by utilizing several deep learning strategies, including data augmentation and transfer learning. The aim was to build a model that would detect abnormalities in chest x-ray images with the highest probability. To do that, different models were built with different features. While making a CNN model, one of the main tasks is to tune the model by changing the hyperparameters and layers so that the model gives out good training and testing results. In our case, three different models were built, and finally, the last one gave out the best-predicted results. From that last model, we got 98% training accuracy, 84% validation, and 81% testing accuracy. The reason behind the final model giving out the best evaluation scores is that it was a well-fitted model. There was no overfitting or underfitting issues. Our aim with this project was to make a tool using the CNN model in R language, which will help detect abnormalities in radiography images. The tool will be able to detect diseases such as Pneumonia, Covid-19, Effusions, Infiltration, Pneumothorax, and others. Because of its high accuracy, this research chose to use supervised multi-class classification techniques as well as Convolutional Neural Networks (CNNs) to classify different chest x-ray images. CNNs are extremely efficient and successful at reducing the number of parameters while maintaining the quality of the primary model. CNNs are also trained to recognize the edges of various objects in any batch of images. CNNs automatically discover the relevant aspects in labeled data and learn the distinguishing features for each class by themselves.

Resolution analysis of thermal neutron radiography based on accelerator-driven compact neutron source

Owing to the immobility of traditional reactors and spallation neutron sources,the demand for compact thermal neutron radiography(CTNR)based on accelerator neutron sources has rapidly increased in industrial applications.Recently,thermal neutron radiography experiments based on a D-T neutron generator performed by Hefei Institutes of Physical Science indicated a significant resolution deviation between the experimental results and the values calculated using the traditional resolution model.The experimental result was up to 23%lower than the calculated result,which hinders the achievement of the design goal of a compact neutron radiography system.A GEANT4 Monte Carlo code was developed to simulate the CTNR process,aiming to identify the key factors leading to resolution deviation.The effects of a low collimation ratio and high-energy neutrons were analyzed based on the neutron beam environment of the CTNR system.The results showed that the deviation was primarily caused by geometric distortion at low collimation ratios and radiation noise induced by highenergy neutrons.Additionally,the theoretical model was modified by considering the imaging position and radiation noise factors.The modified theoretical model was in good agreement with the experimental results,and the maximum deviation was reduced to 4.22%.This can be useful for the high-precision design of CTNR systems.

Geographical Information Visualization on a Panoramic Sphere in an Immersive Environment

In this paper,we propose a novel approach to visualizing global geographical information:a panoramic sphere in an immersive environment.The whole geographical surface can be observed through the rotating of heads as the viewpoint of the panoramic sphere is inside the sphere.We compared three approaches to visualizing the earth for rendering the geographical information in a virtual reality environment.On the tasks of terrestrial and marine geographical information,we compare the visualization effects on a)a globe,b)a flat map and c)a panoramic sphere.Terrestrial geographical information tasks include the area comparison and direction determination.Marine geographical information tasks contain the visualization of sea surface temperature and sea surface currents.For terrestrial geographical information tasks,the experimental results show that the panoramic sphere outperforms the globe and the flat map,with a higher average accuracy and a shorter time.On marine geographical information task,the panoramic sphere visualization is also superior to the flat map and the globe in an immersive environment for the sea surface temperature data and the sea surface current fields.In all three visualization experiments,the panoramic sphere is most preferred by the participants,particularly for global,transcontinental and transoceanic needs.

Transfer learning-based super-resolution in panoramic models for predicting mandibular third molar extraction difficulty: a multi-center study

Background:This study aims to predict the extraction difficulty of mandibular third molars based on panoramic images using transfer learning while employing super-resolution(SR)technology to enhance the feasibility and validity of the prediction.Methods:We reviewed a total of 608 preoperative mandibular third molar panoramic radiographs from two medical facilities:the First Affiliated Hospital of Zhengzhou University(n=509;456 in the training set and 53 in the test set)and the Henan Provincial Dental Hospital(n=99 in the validation set).We conducted a deep-transfer learning network on high-resolution(HR)panoramic radiographs to improve the longitudinal resolution of the images and obtained the SR images.Subsequently,we constructed models named Model-HR and Model-SR using high-dimensional quantitative features extracted through the Least Absolute Shrinkage and Selection Operator method.The models’performances were evaluated using the receiver operating characteristic curve(ROC).To assess the reliability of the model,we compared the results from the test set with those of three dentists.Results:Model-SR outperformed Model-HR(area under the curve(AUC):0.779,sensitivity:85.5%,specificity:60.9%,and accuracy:79.8%vs.AUC:0.753,sensitivity:73.7%,specificity:73.9%,and accuracy:73.7%)in predicting the difficulty of extracting mandibular third molars.Both Model-HR(AUC=0.821,95%CI 0.687–0.956)and Model-SR(AUC=0.963,95%CI 0.921–0.999)demonstrated superior performance compared to expert dentists(highest AUC=0.799,95%CI 0.671–0.927).Conclusions:Model-SR yielded superior predictive performance in determining the difficulty of extracting mandibular third molars when compared with Model-HR and expert dentists’visual assessments.

In situ study on columnar-equiaxed transition and anaxial columnar dendrite growth of Al-15%Cu alloy by synchrotron radiography

Directional solidification of Al-15% （mass fraction） Cu alloy was investigated by in situ and real time radiography which was performed by Shanghai synchrotron radiation facility （SSRF）. The imaging results reveal that columnar to equiaxed transition （CET） is provoked by external thermal disturbance. The detaching and floating of fragments of dendrite arms are the prelude of the transition when the solute boundary layer in front of the solid-liquid interface is thin. And the dendrite triangular tip is the fracture sensitive zone. When the conditions are suitable, new dendrites can sprout and grow up. This kind of dendrite has no obvious stem and is named anaxial columnar dendrites.

Accessory Mental Foramen Misdiagnosed as Radiolucent Tumour by Conventional Dental Radiography

The mental foramen is an important landmark for identifying the mental nerve, and the accessory mental foramen is a rare anatomical variation. This article describes the use of computed tomo- graphy (CT) to detect an accessory mental foramen that was initially misdiagnosed as a radiolu- cent tumour in the right premolar region of a 39-year-old woman by panoramic radiography. The case suggests that preoperative CT should be performed to detect any anatomical variations. This knowledge would help in diagnosis, preoperative planning and prevention of intraoperative nerve or vascular injury.

Rheumatoid Arthritis of the Temporomandibular Joint;Comparison of Digital Panoramic Radiographs Taken Using the Joint Limitation Program [JLA View] and CT Scans

Objective: The aim of this study was to evaluate the efficacy of digital panoramic radiographs using the JLA view pro-gram in cases of rheumatoid arthritis and compare them to CT scans of the patients. Methods: 40 patients with known condition of RA and clinical symptoms in the TMJ were selected for the study. Radiological evaluation included a panoramic radiograph of the TMJs that was taken and a computer tomography of the joints. In the panoramic radio-graphs taken, isolation of the TMJs was done using the JLA view program, while in the CT scans of the patients, all scans were taken with closed mouth, with a distance of 0.5 mm per slice. The parameters examined were: 1) Bony changes of the condyle;2) The position of the condyle in the mandibular fossa;3) The joint space;4) Bony changes of mandibular fossa. Results: There were no statistically significant differences found between the two observers or be-tween the two joints of the same patient [right and left] on the panoramic radiographs. For the case of CT scans there were significant differences between the joint space of right and left joints, while in the ANOVA performed differences were found for the evaluation of the bony changes of the condyle. Conclusion: There were no significant differences between the two radiographic methods selected and therefore when a proper simple radiograph is taken and well evalu-ated, the conclusions drawn from it are well based and there is no need for further

Analysis of Bone Quality on Panoramic Radiograph in Osteoporosis Research by Fractal Dimension

Objective: To Assess the correlation between different quality analysis parameters of trabecular pattern in digital panoramic radiographies and relations with forearm bone mass density (BMD) performed by DXA. Methods: The study was developed using panoramic and peripheral bone densitometry dual energy X-ray absorptiometry (DXA) of 68 patients, 9 males and 59 females (19 - 73 years old). In the panoramic radiographs, evaluation of the trabecular bone morphology through assessment of fractal dimension (FD), connectivity (C) and total number of “bright” pixels (ET) was performed. In DXA, the exam determines the bone mineral density of the forearm to identify who has a high risk of osteoporosis. Statistics analyzed the relationship of these exams and the contribution of dental radiographs in detecting patients at risk for osteoporosis. Results: The average age of subjects was 43.85. In the analysis of trabecular pattern, a significant correlation between the FD, ET and C factors in level of 5% (Pearson correlation test) was found. Correlation tests showed no significant correlation between DF and BMD. Conclusions: The analysis showed correlations with each other, detecting alterations in the trabecular pattern. It cannot be related to BMD with FD but should be taken into account that examining the bone or trabecular alveolar process, when, for example, diagnostic analysis of pre-implant bone quality, is required.

Removal of the Shadow of Cervical Vertebrae from Panoramic X-Ray Images with a Tomosynthesis Method

The purpose of this study is to remove the shadow of cervical vertebrae from dental panoramic x-ray images with a tomosynthesis method and improve the contrast of details in both the teeth and jaw bones. To measure the shift-amount at each angular position that was required for reconstruction of panoramic x-ray images of the dental arch, strip images of a calibration phantom were acquired. Then, a shift-amount table was prepared from these images, and the other shift-amount table, which was used to reconstruct a panoramic image of the cervical vertebrae, was prepared by inverting the curve of the shift-amount table upside down. Using these two tables, images focused on the dental arch and cervical vertebrae of a patient were made with the original strip data of the patient. The shadow of the cervical vertebrae appearing on the image focused on the dental arch was removed using the two above-mentioned images and blurring functions defined at two focusing geometries. The validity of the proposed method was evaluated with clinically acquired data of two patients. The shadow of the cervical vertebrae was successfully eliminated, and the contrast of the front teeth and detailed structures of the jaw bones was improved. The results of the experiments showed that our proposed method was significantly effective in removing the shadow of the cervical vertebrae from conventional panoramic x-ray images.

Registration of Two Dental Panoramic Radiographs

In dental panoramic images, the information on physical changes of alveolar bone or jaw bone is very important to diagnose several diseases. To detect such change, it is useful to compare two panoramic x-ray images acquired at different times. These two images are usually acquired with different conditions in terms of the positioning of the dental arch, and thus these images can be impaired from some geometrical changes related to the scale of the panoramic images and deformation of the teeth and jaw bones. As a result of this, it is very hard to make an accurate registration. To cope with this issue, we developed a dedicated image registration method to match these two images by a newly introduced non-rigid transformation method and registration method using the cross-correlation of localized regions. We evaluated our proposed method with several sets of two images acquired with different geometrical conditions. The material evaluated in this study was a skull phantom. The results of these experiments showed the validity and intrinsic ability of our proposed method in clinical examinations.

Development of Brief Image Quality Evaluation Criteria for Digital OrthoPantomography （OPG） Images in Dental Radiography

An OPG （orthopantmography） is an extra-oral radiographic imaging method which provides a panoramic or wide view of both jaws and teeth on a single image. Digital OPG images provide high contrast with more details of the dentitions. The research main objective was to produce sophisticated and effective criteria that can be used by any radiographer with sound knowledge to identify common errors of digital OPG images and to increase the concern of high frequency of errors to minimize them to give an optimum image quality. The study was designed as retrospective cross sectional study. Hundred digital OPG images are evaluated by three qualified radiographers who had dental radiography experience and four student radiographers. Paired t-test was used to see the difference between the responses of radiographers and student radiographers. Kruskal-Wallis Test was used to see difference between each evaluator. Possible errors of OPG were divided into four main categories （identification, artifact, anatomical coverage and patient positioning）. Each main category consists of sub-categories. Values of subcategories were given according to their importance to get the total of 100% for each main category. The results showed that there is no significant difference between radiographers and student radiographers’ responses and also between each evaluator. Hence it shows that the criteria were an easy understandable and user-friendly tool. And results showed the frequent error category was loss of anatomical coverage and frequent error was absence of positioning the tongue against the palate.

Evaluation of an asymptomatic COVID-19 patient post-surgery with chest radiography: A surgeon’s dilemma

Routine chest radiography is not a requirement in post-surgery cardiac bypass patients.However,the safety of abandoning routine chest radiographs in critically ill patients remains uncertain.Surgery in an asymptomatic coronavirus disease 2019(COVID-19)patient presents additional challenges in postoperative management.Chest radiography remains a valuable tool for assessment of all patients,even a stable one.Management of surgical patients as an emergency in an asymptomatic COVID-19 case remains a surgeon’s dilemma.

Use of Panoramic Radiographs to Detect Signs of Osteoporosis in Edentulous

Objective: The objective is to analyze the importance of panoramic and carpal radiographs for evaluation of edentulous individuals in different ages, demonstrating the contribution of different dental radiographic techniques in detecting patients with signs of osteoporosis. Methods: The study was developed using panoramic and carpal digital radiographs of 30 edentulous women (age ranged 50 - 90 years). The panoramic radiographs were performed to measure the thickness of the mental index—MI and gonial index—GI, and evaluation of the morphology of the mandibular cortical shape (Klemetti classification). The carpal radiographs were performed to measure the cortical thickness of the 2nd, 3rd and 4th metacarpals (metacarpal index). Statistics analyzed the relationship of these indices with the patient’s age and the contribution of dental radiographs in detecting patients at risk for osteoporosis. Results: The average age of subjects was 68.43 years. In the analysis of cortical thickness of the panoramic radiographs, the mean values of mental index (MI) and gonial index (GI) for the age group 50 to 59 years were considered normal values (≥3.1 mm and ≥1.2 mm , respectively). To analyze the morphology of the cortex, the C3 group corresponded to the majority of cases (43.33%). The higher the age range, more morphological changes were observed (sum of the characteristics of groups C2 and C3). For the hand-wrist radiographs, the highest values of the metacarpal index (BMI) were found in the youngest age group (50 - 59 years). Conclusions: There was positive correlation between age and indices of quantitative assessment (IM, IG, BMI) and qualitative (analysis Klemetti) on panoramic and carpal radiographs. Age is a risk factor for the onset of osteoporosis.

Bionic Mosaic Method of Panoramic Image Based on Compound Eye of Fly

To satisfy the requirements of real-time and high quality mosaics, a bionic compound eye visual system was designed by simulating the visual mechanism of a fly compound eye. Several CCD cameras were used in this system to imitate the small eyes of a compound eye. Based on the optical analysis of this system, a direct panoramic image mosaic algorithm was proposed. Several sub-images were collected by the bionic compound eye visual system, and then the system obtained the overlapping proportions of these sub-images and cut the overlap sections of the neighboring images. Thus, a panoramic image with a large field of view was directly mosaicked, which expanded the field and guaranteed the high resolution. The experimental results show that the time consumed by the direct mosaic algorithm is only 2.2% of that by the traditional image mosaic algorithm while guaranteeing mosaic quality. Furthermore, the proposed method effectively solved the problem of misalignment of the mosaic image and eliminated mosaic cracks as a result of the illumination factor and other factors. This method has better real-time properties compared to other methods.

Contrast sensitivity in 14 MeV fast neutron radiography

Fast neutron radiography(FNR) is an effective non-destructive testing technique.Due to the scattering effect and low detection efficiency,the detection limit of FNR under certain conditions cannot be determined.In order to obtain the minimum detectable thickness by FNR,we studied the contrast sensitivity of FNR lead samples,both theoretically and experimentally.We then clarified the relationship between pixel value and irradiation time,and sample materials and thickness.Our experiment,using a4-cm-thick lead sample,verified our theoretical expression of FNR contrast sensitivity.

Research and Implementation of Algorithm for Image Enhancement and Unwrapped Distortion Correction for SLVF Panoramic Night Vision Image

Abstract： Based on digital signal processor（DSP） and field programmable gate array（FPGA） techniques, the architecture of super large view field（SLVF） panoramic night vision image processing hardware platform was established. The panoramic unwrapping and correcting algorithm, up to a full 360°, based on coordinate rotation digital computer （CORDIC） and night vision image enhancement algorithm, based on histogram equalization theory and edge detection theory, was presented in this paper, with the purpose of processing night vision dynamic panoramic annular image. The annular image can be unwrapped and corrected to conventional rectangular panorama by the panoramic image processing algorithm, which uses the pipelined CORDIC configuration to realize a trigonometric function generator with high speed and high precision. Histogram equalization algorithm can perfectly enhance the contrast of the night vision image. Edge detection algorithm can be propitious to find and detect small dim dynamic targets in night vision circumstances. After abundant experiment, the al- gorithm for panoramic image processing and night vision image enhancement is successfully implemented in FPGA and DSP. The panoramic night vision image system is a compact device, with no external rotating parts. And the system can reliably and dynamically detect 360＊ SLVF panoramic night vision image.