Jong-Wook Lim, A. Gupta, H. Park, Jinho Choi, Jaeik Jung, Jaekyu Jang, S. Yeo, J. Ahn, Byeongno Lee, M. Chae, J. Ryu
We designed and developed the vacuum sealed x-ray tube based on carbon nanotube(CNT) field emitter for mobile medical x-ray devices and also design the test bed for CNT x-ray tube. The CNT was synthesized by chemical vapor deposition(CVD) method on a metal alloy substrate. The grown CNT is assembled with a gate and a focuser and then combined into an electron gun(e-gun) through a brazing process. The the e-gun had an aging process inside the vacuum chamber. As a result of aging, the CNT e-gun was able to generate anode current of 1.5 mA at electric field of about 4 V/μm, and field emission current was also stabilized. After the aging process, the e-gun was brazed into a ceramic X-ray tube inside a high-temperature furnace at a vacuum degree of E-06 torr and vacuum sealed. Field emission characteristic was measured using this X-ray tube and compared with an e-gun, and almost similar results were obtained. Incase of Xray tube, we applied a higher electric field while controlling the current at 500ms intervals through pulse driving. As a result, X-ray images of human teeth were successfully acquired using CNT X-ray tubes.
{"title":"X-ray tube based on carbon nanotube field emitter for low dose mini C-arm fluoroscopy","authors":"Jong-Wook Lim, A. Gupta, H. Park, Jinho Choi, Jaeik Jung, Jaekyu Jang, S. Yeo, J. Ahn, Byeongno Lee, M. Chae, J. Ryu","doi":"10.1117/12.2582090","DOIUrl":"https://doi.org/10.1117/12.2582090","url":null,"abstract":"We designed and developed the vacuum sealed x-ray tube based on carbon nanotube(CNT) field emitter for mobile medical x-ray devices and also design the test bed for CNT x-ray tube. The CNT was synthesized by chemical vapor deposition(CVD) method on a metal alloy substrate. The grown CNT is assembled with a gate and a focuser and then combined into an electron gun(e-gun) through a brazing process. The the e-gun had an aging process inside the vacuum chamber. As a result of aging, the CNT e-gun was able to generate anode current of 1.5 mA at electric field of about 4 V/μm, and field emission current was also stabilized. After the aging process, the e-gun was brazed into a ceramic X-ray tube inside a high-temperature furnace at a vacuum degree of E-06 torr and vacuum sealed. Field emission characteristic was measured using this X-ray tube and compared with an e-gun, and almost similar results were obtained. Incase of Xray tube, we applied a higher electric field while controlling the current at 500ms intervals through pulse driving. As a result, X-ray images of human teeth were successfully acquired using CNT X-ray tubes.","PeriodicalId":199502,"journal":{"name":"Medical Imaging 2021: Physics of Medical Imaging","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124870242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Varun Vasudev, Lode De Paepe, Andrew D. A. Maidment, T. Kimpe, L. Platisa, W. Philips, P. Bakic
Dermatoscopes are commonly used to evaluate skin lesions. The rising incidence of skin cancer has led to a wide array of medical imaging devices entering the market, some of which provide the patient the ability to analyze skin lesions themselves. They usually come in the form of smartphone attachments or mobile applications that leverage the optics of the smartphone to acquire the image; and in some cases, even give a preliminary diagnosis. In this digital age these devices look to ease the burden of having to visit a dermatologist multiple times. While these attachments are no doubt very useful, the image sensors used within smartphones are limited in terms of how much information they can process and effectively output to the user. Smartphone sensors are also very small which can result in a less detailed image as opposed to one from a professional camera. Our work is focused on the analysis of the information lost due to the known limitations of smartphone sensors, and its effect on the image appearance. This analysis has been performed using a virtual simulation pipeline for dermatology called VCT-Derma, which contains a module for a proprietary dermatoscope whose optical stack parameters will be adapted to the smartphone sensor specifications mentioned in this manuscript. This manuscript also describes the necessary sensor parameters required for adapting the simulation model, the software used along with any assumptions made, perceived differences in the resulting images, as well as the direction of the ongoing work.
{"title":"Effects of smartphone sensor characteristics on dermatoscopic images: a simulation study","authors":"Varun Vasudev, Lode De Paepe, Andrew D. A. Maidment, T. Kimpe, L. Platisa, W. Philips, P. Bakic","doi":"10.1117/12.2582043","DOIUrl":"https://doi.org/10.1117/12.2582043","url":null,"abstract":"Dermatoscopes are commonly used to evaluate skin lesions. The rising incidence of \u0000skin cancer has led to a wide array of medical imaging devices entering the market, some of which provide the \u0000patient the ability to analyze skin lesions themselves. They usually come in the form of smartphone attachments \u0000or mobile applications that leverage the optics of the smartphone to acquire the image; and in some cases, even \u0000give a preliminary diagnosis. In this digital age these devices look to ease the burden of having to visit a \u0000dermatologist multiple times. While these attachments are no doubt very useful, the image sensors used within \u0000smartphones are limited in terms of how much information they can process and effectively output to the user. \u0000Smartphone sensors are also very small which can result in a less detailed image as opposed to one from a \u0000professional camera. Our work is focused on the analysis of the information lost due to the known limitations of \u0000smartphone sensors, and its effect on the image appearance. This analysis has been performed using a virtual \u0000simulation pipeline for dermatology called VCT-Derma, which contains a module for a proprietary dermatoscope \u0000whose optical stack parameters will be adapted to the smartphone sensor specifications mentioned in this \u0000manuscript. This manuscript also describes the necessary sensor parameters required for adapting the \u0000simulation model, the software used along with any assumptions made, perceived differences in the resulting \u0000images, as well as the direction of the ongoing work.","PeriodicalId":199502,"journal":{"name":"Medical Imaging 2021: Physics of Medical Imaging","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116290864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jinho Choi, A. Gupta, Jaekyu Jang, S. Yeo, Jaeik Jung, M. Chae, Y. Yeon, Byeongno Lee, K. Oh, J. Ahn, Seung Hoon Kim, H. Mok, M. Kong, J. Ryu
We developed a compact vacuum X-ray tube using an alumina body instead of glass. A filament is implanted as a cathode which follows Richardson-Dushman equation. After aging the filament to eliminate impurities on the filament which improves performance of filament before tubing, tube current was obtained from anode voltage of 6kV, 3mA to 40kV, 3.15mA. The pulse high voltage generator is designed and developed to make the tube less stressful. With the ceramic X-ray tube, X-ray images of human breast and teeth phantom were successfully obtained, verifying the potential of the compact alumina vacuum sealed X-ray tube in X-ray application for medical imaging.
{"title":"Characterization of compact alumina vacuum sealed x-ray tube for medical imaging: interpretation with simulation program","authors":"Jinho Choi, A. Gupta, Jaekyu Jang, S. Yeo, Jaeik Jung, M. Chae, Y. Yeon, Byeongno Lee, K. Oh, J. Ahn, Seung Hoon Kim, H. Mok, M. Kong, J. Ryu","doi":"10.1117/12.2582295","DOIUrl":"https://doi.org/10.1117/12.2582295","url":null,"abstract":"We developed a compact vacuum X-ray tube using an alumina body instead of glass. A filament is implanted as a cathode which follows Richardson-Dushman equation. After aging the filament to eliminate impurities on the filament which improves performance of filament before tubing, tube current was obtained from anode voltage of 6kV, 3mA to 40kV, 3.15mA. The pulse high voltage generator is designed and developed to make the tube less stressful. With the ceramic X-ray tube, X-ray images of human breast and teeth phantom were successfully obtained, verifying the potential of the compact alumina vacuum sealed X-ray tube in X-ray application for medical imaging.","PeriodicalId":199502,"journal":{"name":"Medical Imaging 2021: Physics of Medical Imaging","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124284320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Gupta, S. Yeo, Jaekyu Jang, Jaeik Jung, Jinho Choi, Jong-Wook Lim, WooSeob Kim, Junyoung Park, H. Park, M. Chae, Y. Yeon, J. Ahn, Seung Hoon Kim, Namkug Kim, B. Ko, J. Ryu
A microfocus X-ray source based on carbon nanotube (CNT) emitter grown by chemical vapor deposition is presented in this paper. The microfocus X-ray source is developed for the intraoperative specimen radiographic system, which can be used inside the operation theatre and helps reducing the surgery time during breast conserving surgery by confirming the extent of margin on specimen. This high focusing X-ray source is realized by growing CNTs on pointed structures. The field emission characteristic shows that maximum anode current of 1mA, which corresponds to a maximum emission current density of 500 mA/cm2 from the CNT-based point emitter. The optimized parameter for the assembly of electron gun was achieved by using commercially available CST simulation software. Consequently, this microfocus X-ray tube could produce X-ray image of multilayer printed circuit board showing fine lines of integrated circuit.
{"title":"Development of microfocus x-ray source based on CNT emitter for intraoperative specimen radiographic system","authors":"A. Gupta, S. Yeo, Jaekyu Jang, Jaeik Jung, Jinho Choi, Jong-Wook Lim, WooSeob Kim, Junyoung Park, H. Park, M. Chae, Y. Yeon, J. Ahn, Seung Hoon Kim, Namkug Kim, B. Ko, J. Ryu","doi":"10.1117/12.2582087","DOIUrl":"https://doi.org/10.1117/12.2582087","url":null,"abstract":"A microfocus X-ray source based on carbon nanotube (CNT) emitter grown by chemical vapor deposition is presented in this paper. The microfocus X-ray source is developed for the intraoperative specimen radiographic system, which can be used inside the operation theatre and helps reducing the surgery time during breast conserving surgery by confirming the extent of margin on specimen. This high focusing X-ray source is realized by growing CNTs on pointed structures. The field emission characteristic shows that maximum anode current of 1mA, which corresponds to a maximum emission current density of 500 mA/cm2 from the CNT-based point emitter. The optimized parameter for the assembly of electron gun was achieved by using commercially available CST simulation software. Consequently, this microfocus X-ray tube could produce X-ray image of multilayer printed circuit board showing fine lines of integrated circuit.","PeriodicalId":199502,"journal":{"name":"Medical Imaging 2021: Physics of Medical Imaging","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126693085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Gann, E. Abadi, Jocelyn Hoye, T. Sauer, W. Paul Segars, H. Chalian, E. Samei
Radiomic features extracted from CT imaging can be used to quantitively assess COVID-19. The objective of this work was to extract and analyze radiomics features in RT-PRC confirmed COVID-19 cases to identify relevant characteristics for COVID-19 diagnosis, prognosis, and treatment. We measured 29 morphology and second-order statistical-based radiomics features from 310 lung lesions extracted from 48 chest CT cases. Features were evaluated according to their coefficient of variation (CV). We calculated the CV for each feature under two statistical conditions: one with all lesions weighted equally and one with all cases weighted equally. In analyzing the patient data, there were 6.46 lesions-per-case and for 81.25% of cases, the lesions presented with bilateral lung involvement. For all radiomic features examined except ‘energy’, the CV was higher in the lesion distribution than the case distribution. The CV for morphological features were larger than second-order in both distributions, 181% and 85% versus 50% and 42%, respectively. The most variable features were ‘surface area’, ‘ellipsoid volume’, ‘ellipsoid surface area’, ‘volume’, and ‘approximate volume’, which deviated from the mean 173-255% in the lesion distribution and 119-176% in the case distribution. The features with the lowest CV were ‘homogeneity’, ‘discrete compactness’, ‘texture entropy’, ‘sum average’, and ‘elongation’, which deviated less than 31% by case and less than 25% by lesion. Future work will investigate integrating this data with similar studies and other diagnostic and prognostic criterion enhancing the role of CT in detecting and managing COVID-19.
{"title":"An analysis of radiomics features in lung lesions in COVID-19","authors":"A. Gann, E. Abadi, Jocelyn Hoye, T. Sauer, W. Paul Segars, H. Chalian, E. Samei","doi":"10.1117/12.2582296","DOIUrl":"https://doi.org/10.1117/12.2582296","url":null,"abstract":"Radiomic features extracted from CT imaging can be used to quantitively assess COVID-19. The objective of this work was to extract and analyze radiomics features in RT-PRC confirmed COVID-19 cases to identify relevant characteristics for COVID-19 diagnosis, prognosis, and treatment. We measured 29 morphology and second-order statistical-based radiomics features from 310 lung lesions extracted from 48 chest CT cases. Features were evaluated according to their coefficient of variation (CV). We calculated the CV for each feature under two statistical conditions: one with all lesions weighted equally and one with all cases weighted equally. In analyzing the patient data, there were 6.46 lesions-per-case and for 81.25% of cases, the lesions presented with bilateral lung involvement. For all radiomic features examined except ‘energy’, the CV was higher in the lesion distribution than the case distribution. The CV for morphological features were larger than second-order in both distributions, 181% and 85% versus 50% and 42%, respectively. The most variable features were ‘surface area’, ‘ellipsoid volume’, ‘ellipsoid surface area’, ‘volume’, and ‘approximate volume’, which deviated from the mean 173-255% in the lesion distribution and 119-176% in the case distribution. The features with the lowest CV were ‘homogeneity’, ‘discrete compactness’, ‘texture entropy’, ‘sum average’, and ‘elongation’, which deviated less than 31% by case and less than 25% by lesion. Future work will investigate integrating this data with similar studies and other diagnostic and prognostic criterion enhancing the role of CT in detecting and managing COVID-19.","PeriodicalId":199502,"journal":{"name":"Medical Imaging 2021: Physics of Medical Imaging","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126200371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Najib Akram Maheen Aboobacker, G. González, Fengchao Zhang, J. Wanek, P. Xue, G. Rao, Dong Hye Ye
In general X-ray radiography, inconsistency of brightness and contrast in initial presentation is a common complaint from radiologists. Inconsistencies, which may be a result of variations in patient positioning, dose, protocol selection and implant could lead to additional workflow by technologists and radiologists to adjust the images. To tackle this challenge posed by conventional histogram-based display approach, an AI Based Brightness Contrast (AI BC) algorithm is proposed to improve the consistency in presentation by using a residual neural network trained to classify X-ray images based on N by M grid of brightness and contrast combinations. More than 30,000 unique images from sites in US, Ireland and Sweden covering 31 anatomy/view combinations were used for training. The model achieved an average test accuracy of 99.2% on a set of 2700 images. AI BC algorithm uses the model to classify and adjust images to achieve a reference look and then further adjust to achieve user preference. Quantitative evaluation using ROI based metrics on a set of twelve wrist images showed a 53% reduction in mean pixel intensity variation and a 39% reduction in bone-tissue contrast variation. A study with application specialists adjusting image presentation of 30 images covering 3 anatomies (foot, abdomen and knee) was performed. On average, the application specialists took ~20 minutes to adjust the conventional set, whereas they took ~10 minutes for the AI BC set. The proposed approach demonstrates the feasibility of using deep learning technique to reduce inconsistency in initial display presentation and improve user workflow.
{"title":"Improving presentation consistency of radiographic images using deep learning","authors":"Najib Akram Maheen Aboobacker, G. González, Fengchao Zhang, J. Wanek, P. Xue, G. Rao, Dong Hye Ye","doi":"10.1117/12.2582026","DOIUrl":"https://doi.org/10.1117/12.2582026","url":null,"abstract":"In general X-ray radiography, inconsistency of brightness and contrast in initial presentation is a common complaint from radiologists. Inconsistencies, which may be a result of variations in patient positioning, dose, protocol selection and implant could lead to additional workflow by technologists and radiologists to adjust the images. To tackle this challenge posed by conventional histogram-based display approach, an AI Based Brightness Contrast (AI BC) algorithm is proposed to improve the consistency in presentation by using a residual neural network trained to classify X-ray images based on N by M grid of brightness and contrast combinations. More than 30,000 unique images from sites in US, Ireland and Sweden covering 31 anatomy/view combinations were used for training. The model achieved an average test accuracy of 99.2% on a set of 2700 images. AI BC algorithm uses the model to classify and adjust images to achieve a reference look and then further adjust to achieve user preference. Quantitative evaluation using ROI based metrics on a set of twelve wrist images showed a 53% reduction in mean pixel intensity variation and a 39% reduction in bone-tissue contrast variation. A study with application specialists adjusting image presentation of 30 images covering 3 anatomies (foot, abdomen and knee) was performed. On average, the application specialists took ~20 minutes to adjust the conventional set, whereas they took ~10 minutes for the AI BC set. The proposed approach demonstrates the feasibility of using deep learning technique to reduce inconsistency in initial display presentation and improve user workflow.","PeriodicalId":199502,"journal":{"name":"Medical Imaging 2021: Physics of Medical Imaging","volume":"57 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133976219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Junyoung Park, A. Gupta, WooSeob Kim, Jong-Wook Lim, S. Yeo, Dongkeun Kim, Chang-Won Jeong, K. Yoon, Seungryong Cho, J. Ahn, M. Mativenga, J. Ryu
In order to diagnose diseases in complex areas such as the chest, an X-ray system of a suitable type is required. Chest tomosynthesis, which acquires a reconstructed 3D image by taking X-ray images from various angles, is one of the best image acquisition technologies in use. However, one major disadvantage of tomosynthesis systems with a single X-ray source is the motion blur which occurs when the source moves or rotates to change the acquisition angle. To overcome this, we report a stationary digital tomosynthesis system, which uses 85 field-emission type X-ray sources based on carbon nanotubes (CNTs). By using CNT-based electronic emitters, it is possible to miniaturize and digitize the X-ray system. This system is designed such that a maximum of 120 kV can be applied to the anode to obtain chest X-ray images. The field emission characteristics of the CNT-based emitters are measured, and X-ray images were obtained using the stationary multi X-ray source system, confirming its applicability to chest Tomosynthesis.
{"title":"Stationary multi X-ray source system with carbon nanotube emitters for digital tomosynthesis","authors":"Junyoung Park, A. Gupta, WooSeob Kim, Jong-Wook Lim, S. Yeo, Dongkeun Kim, Chang-Won Jeong, K. Yoon, Seungryong Cho, J. Ahn, M. Mativenga, J. Ryu","doi":"10.1117/12.2582280","DOIUrl":"https://doi.org/10.1117/12.2582280","url":null,"abstract":"In order to diagnose diseases in complex areas such as the chest, an X-ray system of a suitable type is required. Chest tomosynthesis, which acquires a reconstructed 3D image by taking X-ray images from various angles, is one of the best image acquisition technologies in use. However, one major disadvantage of tomosynthesis systems with a single X-ray source is the motion blur which occurs when the source moves or rotates to change the acquisition angle. To overcome this, we report a stationary digital tomosynthesis system, which uses 85 field-emission type X-ray sources based on carbon nanotubes (CNTs). By using CNT-based electronic emitters, it is possible to miniaturize and digitize the X-ray system. This system is designed such that a maximum of 120 kV can be applied to the anode to obtain chest X-ray images. The field emission characteristics of the CNT-based emitters are measured, and X-ray images were obtained using the stationary multi X-ray source system, confirming its applicability to chest Tomosynthesis.","PeriodicalId":199502,"journal":{"name":"Medical Imaging 2021: Physics of Medical Imaging","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130893727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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