The objective of this work was to study the use of standard equipment in amplitude and frequency tests of dental scaler tip according to ISO 18397.Four types of standard equipment:a laser displacement sensor,a microsc...The objective of this work was to study the use of standard equipment in amplitude and frequency tests of dental scaler tip according to ISO 18397.Four types of standard equipment:a laser displacement sensor,a microscope,a tachometer,and an ultrasonic frequency meter,were experimentally investigated to test a tip.The standard laser displacement sensor and the standard microscope were used to test the unloaded amplitude of the scaler tip.It was found that two types of standard equipment were able to measure the unloaded amplitude of the tip.The standard microscope was also employed for the loaded amplitude test.This test was performed by pressing the scaler tip with a load of 1 N,which was measured by a load cell set.The peak-to-peak amplitude found from the test was 116.7 m.The frequency test of the scaler tip was conducted using the standard laser displacement sensor,the tachometer and the ultrasonic frequency meter.All three types of standard equipment were found to be able to test the frequency of the tip without cooling liquid.Nevertheless,only the standard tachometer was capable of measuring the frequency of the tip with cooling liquid applied.展开更多
Chronological age estimation using panoramic dental X-ray images is an essential task in forensic sciences.Various statistical approaches have proposed by considering the teeth and mandible.However,building automated ...Chronological age estimation using panoramic dental X-ray images is an essential task in forensic sciences.Various statistical approaches have proposed by considering the teeth and mandible.However,building automated dental age estimation based on machine learning techniques needs more research efforts.In this paper,an automated dental age estimation is proposed using transfer learning.In the proposed approach,features are extracted using two deep neural networks namely,AlexNet and ResNet.Several classifiers are proposed to perform the classification task including decision tree,k-nearest neighbor,linear discriminant,and support vector machine.The proposed approach is evaluated using a number of suitable performance metrics using a dataset that contains 1429 dental X-ray images.The obtained results show that the proposed approach has a promising performance.展开更多
This review highlights the recent advances in X-ray microcomputed tomography （Micro-CT） applied in dental research. It summarizes Micro-CT applications in mea- surement of enamel thickness, root canal morphology, ev...This review highlights the recent advances in X-ray microcomputed tomography （Micro-CT） applied in dental research. It summarizes Micro-CT applications in mea- surement of enamel thickness, root canal morphology, evaluation of root canal preparation, craniofacial skeletalstructure, micro finite element modeling, dental tissue engineering, mineral density of dental hard tissues and about dental implants. Details of studies in each of these areas are highlighted along with the advantages of Micro-CT, and finally a summary of the future applications of Micro-CT in dental research is given.展开更多
Modern medicine is unthinkable without X-rays. Accurate diagnosis, leading to effective treatment, is largely based on precise X-ray examinations. The creation of new, modern equipment and various medical procedures t...Modern medicine is unthinkable without X-rays. Accurate diagnosis, leading to effective treatment, is largely based on precise X-ray examinations. The creation of new, modern equipment and various medical procedures that meet the increased requirements are a priority in our time. X-ray examinations are of particular importance for the orthopedic and traumatological clinics, where they provide information about presence of a fracture in the patient’s body, about the concrete operation performed or about the effect of a suitable treatment. Along with their benefits X-rays have also a harmful effect. This requires special care to protect from this radiation. In this direction, research is constantly being done to improve the quality of radiation protection. Park MR, Lee KM and co-authors, compare the dose load obtained using C-arm and O-arm X-ray systems (which have the capability of combined 2D fluoroscopy and 3D computed tomography imaging). In their study, an orthopedic surgical procedure using C-arm and O-arm systems in 2D fluoroscopy modes was simulated. The radiation doses to susceptible organs of the operators were investigated. He results obtained show that the O-arm system delivered higher doses to the sensitive organs of the operator in all configurations [1]. The article of Stephen Balte briefly reviews the available technologies for measuring or estimation of patient skin dose in the interventional fluoroscopic environment, created by various X-ray equipment including C-arm systems. Given that many patients require multiple procedures, this documentation also aids in the planning of follow up visits [2]. Chong Hing Wong, Yoshihisa Kotani and co-authors evaluate the radiation exposures (RE) to the patient and surgeon during minimally invasive lumbar spine surgery with instrumentation using C-arm image intensifier or O-arm intraoperative CT. The results they get are in favor of the O-arm system [3]. The article “Virtual fluoroscopy for intraoperative C-arm positioning and radiation dose reduction” discusses positioning of an intraoperative C-arm system to achieve clear visualization of a particular anatomical feature by a system for virtual fluoroscopy (called FluoroSim) that could dramatically reduce time and received dose during the procedures. FluoroSim was found to reduce the radiation exposure required for C-arm positioning without reducing positioning time or accuracy, providing a potentially valuable tool to assist surgeons [4]. In our study, we performed practical measurements to show how the patient can be treated by applying most effective radiation protection when using a mobile C-arm X-ray system. For the study, we used exposure upon a phantom placed on the patient’s table. For an X-ray shielding, we used a protective apron with a lead equivalent of 1 mm, placed in two layers on the phantom. In each subsequent series of exposures, the protective apron was placed on the phantom, in a different position relative to the X-ray beam. The general conclusion of our study is that in order to obtain maximum protection from scattered radiation when using C-arm X-ray systems, the patient must be protected by a shielding with a suitable lead equivalent for the procedure performed which must be placed between patient’s body and X-ray tube, perpendicular to the X-ray beam pointed toward the region of interest.展开更多
X-ray inspection equipment is divided into small baggage inspection equipment and large cargo inspection equipment.In the case of inspection using X-ray scanning equipment,it is possible to identify the contents of go...X-ray inspection equipment is divided into small baggage inspection equipment and large cargo inspection equipment.In the case of inspection using X-ray scanning equipment,it is possible to identify the contents of goods,unauthorized transport,or hidden goods in real-time by-passing cargo through X-rays without opening it.In this paper,we propose a system for detecting dangerous objects in X-ray images using the Cascade Region-based Convolutional Neural Network(Cascade R-CNN)model,and the data used for learning consists of dangerous goods,storage media,firearms,and knives.In addition,to minimize the overfitting problem caused by the lack of data to be used for artificial intelligence(AI)training,data samples are increased by using the CP(copy-paste)algorithm on the existing data.It also solves the data labeling problem by mixing supervised and semi-supervised learning.The four comparative models to be used in this study are Faster Regionbased Convolutional Neural Networks Residual2 Network-101(Faster R-CNN_Res2Net-101)supervised learning,Cascade R-CNN_Res2Net-101_supervised learning,Cascade Region-based Convolutional Neural Networks Composite Backbone Network V2(CBNetV2)Network-101(Cascade R-CNN_CBNetV2Net-101)_supervised learning,and Cascade RCNN_CBNetV2-101_semi-supervised learning which are then compared and evaluated.As a result of comparing the performance of the four models in this paper,in case of Cascade R-CNN_CBNetV2-101_semi-supervised learning,Average Precision(AP)(Intersection over Union(IoU)=0.5):0.7%,AP(IoU=0.75):1.0%than supervised learning,Recall:0.8%higher.展开更多
We conducted experiments of oversensing generation of pacemaker (PM) and X-irradiation direction dependency of PM, and examined the oversensing suppression method, using 8 different types of PMs. It was found out from...We conducted experiments of oversensing generation of pacemaker (PM) and X-irradiation direction dependency of PM, and examined the oversensing suppression method, using 8 different types of PMs. It was found out from this experiment that oversensing would occur when some conditions (X-irradiation direction, X-irradiation intensity) are met. Oversensing occurred with the most low irradiation conditions (kV × mA) when PM was irradiated at 90° (vertically to C-MOS;Complementary Metal Oxide Semiconductor). The acuter the angle of irradiation is (α > 90° < α), the higher the irradiation conditions (kV × mA) at which oversensing start to occur. In plain X-ray photography, oversensing was confirmed under the irradiation conditions of (cervical spine, thoracic spine, lateral thoracic spine, rib, shoulder joint, collarbone, humerus, and chest).Once the irradiation angle and irradiation conditions (kV × mA) are available, oversensing is predictable to some extent. Our findings will help to predict oversensing generation of plain X-ray photography and suppress oversensing. Oversensing can be suppressed in most of the radiography by lowering tube current to 100 mA, but a 1.0 mm High-Density Tungsten Sheet must be put on PM in high tube voltage radiography.展开更多
CuKβ radiation with a wavelength of λ = 1.3923 ? is recommended for crystal structure determination from X-ray powder diffraction using the Rietfeld method. A highly sensitive image plate detector is able to collect...CuKβ radiation with a wavelength of λ = 1.3923 ? is recommended for crystal structure determination from X-ray powder diffraction using the Rietfeld method. A highly sensitive image plate detector is able to collect enough intensity to record a brilliant X-ray powder pattern in a reasonable time, compared to CuKα1 radiation used today. Especially atomic displacement coefficients could be determined more precisely with the much greater number of reflections recorded. A double-radius Guinier camera attached to a micro-focus rotating anode tube ensures increased brilliance besides high resolution. A simple construction specification is presented to make smart cylindrically bent Ge(111) or Si(111) X-ray monochromators that deliver focused CuKβ radiation. The highly linear response of image plate detectors allows removing of fluorescence radiation simply as background of the pattern. The proposed equipment is a cost-efficient alternative to a liquid gallium-metal-jet X-ray source with maximum power load and a similar wavelength of λ(GaKα1) = 1.34013 ?.展开更多
Automated and autonomous decisions of image classification systems have essential applicability in this modern age even.Image-based decisions are commonly taken through explicit or auto-feature engineering of images.I...Automated and autonomous decisions of image classification systems have essential applicability in this modern age even.Image-based decisions are commonly taken through explicit or auto-feature engineering of images.In forensic radiology,auto decisions based on images significantly affect the automation of various tasks.This study aims to assist forensic radiology in its biological profile estimation when only bones are left.A benchmarked dataset Radiology Society of North America(RSNA)has been used for research and experiments.Additionally,a locally developed dataset has also been used for research and experiments to cross-validate the results.A Convolutional Neural Network(CNN)-based model named computer vision and image processing-net(CVIP-Net)has been proposed to learn and classify image features.Experiments have also been performed on state-of-the-art pertained models,which are alex_net,inceptionv_3,google_net,Residual Network(resnet)_50,and Visual Geometry Group(VGG)-19.Experiments proved that the proposed CNN model is more accurate than other models when panoramic dental x-ray images are used to identify age and gender.The specially designed CNN-based achieved results in terms of standard evaluation measures including accuracy(98.90%),specificity(97.99%),sensitivity(99.34%),and Area under the Curve(AUC)-value(0.99)on the locally developed dataset to detect age.The classification rates of the proposed model for gender estimation were 99.57%,97.67%,98.99%,and 0.98,achieved in terms of accuracy,specificity,sensitivity,and AUC-value,respectively,on the local dataset.The classification rates of the proposed model for age estimation were 96.80%,96.80%,97.03%,and 0.99 achieved in terms of accuracy,specificity,sensitivity,and AUC-value,respectively,on the RSNA dataset.展开更多
文摘The objective of this work was to study the use of standard equipment in amplitude and frequency tests of dental scaler tip according to ISO 18397.Four types of standard equipment:a laser displacement sensor,a microscope,a tachometer,and an ultrasonic frequency meter,were experimentally investigated to test a tip.The standard laser displacement sensor and the standard microscope were used to test the unloaded amplitude of the scaler tip.It was found that two types of standard equipment were able to measure the unloaded amplitude of the tip.The standard microscope was also employed for the loaded amplitude test.This test was performed by pressing the scaler tip with a load of 1 N,which was measured by a load cell set.The peak-to-peak amplitude found from the test was 116.7 m.The frequency test of the scaler tip was conducted using the standard laser displacement sensor,the tachometer and the ultrasonic frequency meter.All three types of standard equipment were found to be able to test the frequency of the tip without cooling liquid.Nevertheless,only the standard tachometer was capable of measuring the frequency of the tip with cooling liquid applied.
文摘Chronological age estimation using panoramic dental X-ray images is an essential task in forensic sciences.Various statistical approaches have proposed by considering the teeth and mandible.However,building automated dental age estimation based on machine learning techniques needs more research efforts.In this paper,an automated dental age estimation is proposed using transfer learning.In the proposed approach,features are extracted using two deep neural networks namely,AlexNet and ResNet.Several classifiers are proposed to perform the classification task including decision tree,k-nearest neighbor,linear discriminant,and support vector machine.The proposed approach is evaluated using a number of suitable performance metrics using a dataset that contains 1429 dental X-ray images.The obtained results show that the proposed approach has a promising performance.
文摘This review highlights the recent advances in X-ray microcomputed tomography （Micro-CT） applied in dental research. It summarizes Micro-CT applications in mea- surement of enamel thickness, root canal morphology, evaluation of root canal preparation, craniofacial skeletalstructure, micro finite element modeling, dental tissue engineering, mineral density of dental hard tissues and about dental implants. Details of studies in each of these areas are highlighted along with the advantages of Micro-CT, and finally a summary of the future applications of Micro-CT in dental research is given.
文摘Modern medicine is unthinkable without X-rays. Accurate diagnosis, leading to effective treatment, is largely based on precise X-ray examinations. The creation of new, modern equipment and various medical procedures that meet the increased requirements are a priority in our time. X-ray examinations are of particular importance for the orthopedic and traumatological clinics, where they provide information about presence of a fracture in the patient’s body, about the concrete operation performed or about the effect of a suitable treatment. Along with their benefits X-rays have also a harmful effect. This requires special care to protect from this radiation. In this direction, research is constantly being done to improve the quality of radiation protection. Park MR, Lee KM and co-authors, compare the dose load obtained using C-arm and O-arm X-ray systems (which have the capability of combined 2D fluoroscopy and 3D computed tomography imaging). In their study, an orthopedic surgical procedure using C-arm and O-arm systems in 2D fluoroscopy modes was simulated. The radiation doses to susceptible organs of the operators were investigated. He results obtained show that the O-arm system delivered higher doses to the sensitive organs of the operator in all configurations [1]. The article of Stephen Balte briefly reviews the available technologies for measuring or estimation of patient skin dose in the interventional fluoroscopic environment, created by various X-ray equipment including C-arm systems. Given that many patients require multiple procedures, this documentation also aids in the planning of follow up visits [2]. Chong Hing Wong, Yoshihisa Kotani and co-authors evaluate the radiation exposures (RE) to the patient and surgeon during minimally invasive lumbar spine surgery with instrumentation using C-arm image intensifier or O-arm intraoperative CT. The results they get are in favor of the O-arm system [3]. The article “Virtual fluoroscopy for intraoperative C-arm positioning and radiation dose reduction” discusses positioning of an intraoperative C-arm system to achieve clear visualization of a particular anatomical feature by a system for virtual fluoroscopy (called FluoroSim) that could dramatically reduce time and received dose during the procedures. FluoroSim was found to reduce the radiation exposure required for C-arm positioning without reducing positioning time or accuracy, providing a potentially valuable tool to assist surgeons [4]. In our study, we performed practical measurements to show how the patient can be treated by applying most effective radiation protection when using a mobile C-arm X-ray system. For the study, we used exposure upon a phantom placed on the patient’s table. For an X-ray shielding, we used a protective apron with a lead equivalent of 1 mm, placed in two layers on the phantom. In each subsequent series of exposures, the protective apron was placed on the phantom, in a different position relative to the X-ray beam. The general conclusion of our study is that in order to obtain maximum protection from scattered radiation when using C-arm X-ray systems, the patient must be protected by a shielding with a suitable lead equivalent for the procedure performed which must be placed between patient’s body and X-ray tube, perpendicular to the X-ray beam pointed toward the region of interest.
文摘X-ray inspection equipment is divided into small baggage inspection equipment and large cargo inspection equipment.In the case of inspection using X-ray scanning equipment,it is possible to identify the contents of goods,unauthorized transport,or hidden goods in real-time by-passing cargo through X-rays without opening it.In this paper,we propose a system for detecting dangerous objects in X-ray images using the Cascade Region-based Convolutional Neural Network(Cascade R-CNN)model,and the data used for learning consists of dangerous goods,storage media,firearms,and knives.In addition,to minimize the overfitting problem caused by the lack of data to be used for artificial intelligence(AI)training,data samples are increased by using the CP(copy-paste)algorithm on the existing data.It also solves the data labeling problem by mixing supervised and semi-supervised learning.The four comparative models to be used in this study are Faster Regionbased Convolutional Neural Networks Residual2 Network-101(Faster R-CNN_Res2Net-101)supervised learning,Cascade R-CNN_Res2Net-101_supervised learning,Cascade Region-based Convolutional Neural Networks Composite Backbone Network V2(CBNetV2)Network-101(Cascade R-CNN_CBNetV2Net-101)_supervised learning,and Cascade RCNN_CBNetV2-101_semi-supervised learning which are then compared and evaluated.As a result of comparing the performance of the four models in this paper,in case of Cascade R-CNN_CBNetV2-101_semi-supervised learning,Average Precision(AP)(Intersection over Union(IoU)=0.5):0.7%,AP(IoU=0.75):1.0%than supervised learning,Recall:0.8%higher.
文摘We conducted experiments of oversensing generation of pacemaker (PM) and X-irradiation direction dependency of PM, and examined the oversensing suppression method, using 8 different types of PMs. It was found out from this experiment that oversensing would occur when some conditions (X-irradiation direction, X-irradiation intensity) are met. Oversensing occurred with the most low irradiation conditions (kV × mA) when PM was irradiated at 90° (vertically to C-MOS;Complementary Metal Oxide Semiconductor). The acuter the angle of irradiation is (α > 90° < α), the higher the irradiation conditions (kV × mA) at which oversensing start to occur. In plain X-ray photography, oversensing was confirmed under the irradiation conditions of (cervical spine, thoracic spine, lateral thoracic spine, rib, shoulder joint, collarbone, humerus, and chest).Once the irradiation angle and irradiation conditions (kV × mA) are available, oversensing is predictable to some extent. Our findings will help to predict oversensing generation of plain X-ray photography and suppress oversensing. Oversensing can be suppressed in most of the radiography by lowering tube current to 100 mA, but a 1.0 mm High-Density Tungsten Sheet must be put on PM in high tube voltage radiography.
文摘CuKβ radiation with a wavelength of λ = 1.3923 ? is recommended for crystal structure determination from X-ray powder diffraction using the Rietfeld method. A highly sensitive image plate detector is able to collect enough intensity to record a brilliant X-ray powder pattern in a reasonable time, compared to CuKα1 radiation used today. Especially atomic displacement coefficients could be determined more precisely with the much greater number of reflections recorded. A double-radius Guinier camera attached to a micro-focus rotating anode tube ensures increased brilliance besides high resolution. A simple construction specification is presented to make smart cylindrically bent Ge(111) or Si(111) X-ray monochromators that deliver focused CuKβ radiation. The highly linear response of image plate detectors allows removing of fluorescence radiation simply as background of the pattern. The proposed equipment is a cost-efficient alternative to a liquid gallium-metal-jet X-ray source with maximum power load and a similar wavelength of λ(GaKα1) = 1.34013 ?.
文摘Automated and autonomous decisions of image classification systems have essential applicability in this modern age even.Image-based decisions are commonly taken through explicit or auto-feature engineering of images.In forensic radiology,auto decisions based on images significantly affect the automation of various tasks.This study aims to assist forensic radiology in its biological profile estimation when only bones are left.A benchmarked dataset Radiology Society of North America(RSNA)has been used for research and experiments.Additionally,a locally developed dataset has also been used for research and experiments to cross-validate the results.A Convolutional Neural Network(CNN)-based model named computer vision and image processing-net(CVIP-Net)has been proposed to learn and classify image features.Experiments have also been performed on state-of-the-art pertained models,which are alex_net,inceptionv_3,google_net,Residual Network(resnet)_50,and Visual Geometry Group(VGG)-19.Experiments proved that the proposed CNN model is more accurate than other models when panoramic dental x-ray images are used to identify age and gender.The specially designed CNN-based achieved results in terms of standard evaluation measures including accuracy(98.90%),specificity(97.99%),sensitivity(99.34%),and Area under the Curve(AUC)-value(0.99)on the locally developed dataset to detect age.The classification rates of the proposed model for gender estimation were 99.57%,97.67%,98.99%,and 0.98,achieved in terms of accuracy,specificity,sensitivity,and AUC-value,respectively,on the local dataset.The classification rates of the proposed model for age estimation were 96.80%,96.80%,97.03%,and 0.99 achieved in terms of accuracy,specificity,sensitivity,and AUC-value,respectively,on the RSNA dataset.
