Medical Imaging 2021: Physics of Medical Imaging最新文献

X-ray tube based on carbon nanotube field emitter for low dose mini C-arm fluoroscopy 基于碳纳米管场发射器的x射线管用于小剂量微型c臂透视

Medical Imaging 2021: Physics of Medical Imaging Pub Date : 2021-02-15 DOI: 10.1117/12.2582090

Jong-Wook Lim, A. Gupta, H. Park, Jinho Choi, Jaeik Jung, Jaekyu Jang, S. Yeo, J. Ahn, Byeongno Lee, M. Chae, J. Ryu

{"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}

引用次数: 2

Effects of smartphone sensor characteristics on dermatoscopic images: a simulation study 智能手机传感器特性对皮肤镜图像的影响:模拟研究

Medical Imaging 2021: Physics of Medical Imaging Pub Date : 2021-02-15 DOI: 10.1117/12.2582043

Varun Vasudev, Lode De Paepe, Andrew D. A. Maidment, T. Kimpe, L. Platisa, W. Philips, P. Bakic

{"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}

引用次数: 0

Characterization of compact alumina vacuum sealed x-ray tube for medical imaging: interpretation with simulation program 医学成像用紧凑氧化铝真空密封x射线管的特性:用模拟程序解释

Medical Imaging 2021: Physics of Medical Imaging Pub Date : 2021-02-15 DOI: 10.1117/12.2582295

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

引用次数: 1

Development of microfocus x-ray source based on CNT emitter for intraoperative specimen radiographic system 术中标本照相系统中基于CNT发射器的微聚焦x射线源的研制

Medical Imaging 2021: Physics of Medical Imaging Pub Date : 2021-02-15 DOI: 10.1117/12.2582087

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

引用次数: 1

An analysis of radiomics features in lung lesions in COVID-19 新冠肺炎肺部病变放射组学特征分析

Medical Imaging 2021: Physics of Medical Imaging Pub Date : 2021-02-15 DOI: 10.1117/12.2582296

A. Gann, E. Abadi, Jocelyn Hoye, T. Sauer, W. Paul Segars, H. Chalian, E. Samei

{"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}

引用次数: 0

Improving presentation consistency of radiographic images using deep learning 利用深度学习提高放射图像的呈现一致性

Medical Imaging 2021: Physics of Medical Imaging Pub Date : 2021-02-15 DOI: 10.1117/12.2582026

Najib Akram Maheen Aboobacker, G. González, Fengchao Zhang, J. Wanek, P. Xue, G. Rao, Dong Hye Ye

{"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}

引用次数: 0

Stationary multi X-ray source system with carbon nanotube emitters for digital tomosynthesis 固定式多x射线源系统与碳纳米管发射器用于数字断层合成

Medical Imaging 2021: Physics of Medical Imaging Pub Date : 2021-02-15 DOI: 10.1117/12.2582280

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

引用次数: 3