Pub Date : 2019-12-01DOI: 10.1109/IST48021.2019.9010183
Zainab Husain, P. Liatsis
Electrical Impedance Tomography (EIT) is a method of imaging the impedance distribution inside a non-homogeneous medium based on current or voltage measurements on its surface. Being a non-invasive and non-ionizing image modality, its application can be extended to a multitude of areas, including robotics and specifically, tactile sensing. The use of EIT, however, is limited by the complexity of the inverse image reconstruction problem, which is non-linear and ill-posed. In this contribution, we propose a data-driven approach to image reconstruction, using Neural Networks. Specifically, the image containing the target object is divided into partially overlapping sub-images, where each sub-image is modelled with a bi-variate polynomial. The forward problem is solved using the EIDORS toolbox in MATLAB, thus resulting to a set of voltage measurements. A set of feedforward neural networks, one for each sub-image, are then trained using the voltage inputs and the target polynomial coefficients to perform image reconstruction. The simulation experiments demonstrate promising performance for the case of a 2D square object in a noisy background.
{"title":"A neural network-based local decomposition approach for image reconstruction in Electrical Impedance Tomography","authors":"Zainab Husain, P. Liatsis","doi":"10.1109/IST48021.2019.9010183","DOIUrl":"https://doi.org/10.1109/IST48021.2019.9010183","url":null,"abstract":"Electrical Impedance Tomography (EIT) is a method of imaging the impedance distribution inside a non-homogeneous medium based on current or voltage measurements on its surface. Being a non-invasive and non-ionizing image modality, its application can be extended to a multitude of areas, including robotics and specifically, tactile sensing. The use of EIT, however, is limited by the complexity of the inverse image reconstruction problem, which is non-linear and ill-posed. In this contribution, we propose a data-driven approach to image reconstruction, using Neural Networks. Specifically, the image containing the target object is divided into partially overlapping sub-images, where each sub-image is modelled with a bi-variate polynomial. The forward problem is solved using the EIDORS toolbox in MATLAB, thus resulting to a set of voltage measurements. A set of feedforward neural networks, one for each sub-image, are then trained using the voltage inputs and the target polynomial coefficients to perform image reconstruction. The simulation experiments demonstrate promising performance for the case of a 2D square object in a noisy background.","PeriodicalId":117219,"journal":{"name":"2019 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134026080","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}
The temperature field distribution of the oven directly affects the quality of the baked food. The traditional oven performance research mainly focuses on the heating method in the simulated oven, lacking quantitative analysis of the baked food. In this paper, the digital image processing technology is used to segment and extract different baking status of the baked food in order to qualitatively and quantitatively expresses the internal temperature field distribution of the oven. Firstly, the image of baked food is captured by high-definition digital camera. And then it will be preprocessed to obtain a denoised image with only baked food area. Thirdly, the simple linear iterative clustering (SLIC) segmentation is used to extract the different baking status. The experimental results show that the simple linear iterative clustering (SLIC) segmentation algorithm can digitally express the baking status in the form of superpixels. The proposed method can qualitatively and quantitatively reflect the distribution of the temperature field inside the oven corresponding to the baked food image, which provide a basis for further evaluation of the heat distribution field inside the oven.
{"title":"Baking Status Characterization of Baked Food Image Based on Superpixel Segmentation","authors":"Conghui Wang, Bochuan Hou, Jing Shi, Jianhua Yang, Boning Wu, Zixing Fu, Kun Fang","doi":"10.1109/IST48021.2019.9010460","DOIUrl":"https://doi.org/10.1109/IST48021.2019.9010460","url":null,"abstract":"The temperature field distribution of the oven directly affects the quality of the baked food. The traditional oven performance research mainly focuses on the heating method in the simulated oven, lacking quantitative analysis of the baked food. In this paper, the digital image processing technology is used to segment and extract different baking status of the baked food in order to qualitatively and quantitatively expresses the internal temperature field distribution of the oven. Firstly, the image of baked food is captured by high-definition digital camera. And then it will be preprocessed to obtain a denoised image with only baked food area. Thirdly, the simple linear iterative clustering (SLIC) segmentation is used to extract the different baking status. The experimental results show that the simple linear iterative clustering (SLIC) segmentation algorithm can digitally express the baking status in the form of superpixels. The proposed method can qualitatively and quantitatively reflect the distribution of the temperature field inside the oven corresponding to the baked food image, which provide a basis for further evaluation of the heat distribution field inside the oven.","PeriodicalId":117219,"journal":{"name":"2019 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133159357","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}
Pub Date : 2019-12-01DOI: 10.1109/IST48021.2019.9010457
Emmanouil G Spanakis, D. Takács, A. Karantanas, D. Petrovska-Delacrétaz, Claude Bauzou, Iacob Crucianu, Aymen Mtibaa, Mohamed Amine Hmani, M. Kockmann, Christian Narr, Elnar Hajiyev
The purpose of this work is to present a solution combining user-friendliness and cost-effectiveness use of audio (speech) & visual (video/image) biometrics, for eHealth, able to achieve better accuracy and increase the ability to avoid counterfeiting. This work shows the evaluation results for an eHealth pilot study that tested the security, privacy, usability and cost-effective features of a user authentication platform for the management of sensitive heterogeneous multi-scale medical data (i.e. medical imaging such as MRI/CT scans, physical reports, and laboratory results), through easy acquisition of biometric data via laptops, and tablets equipped with cameras and microphones. Regarding the user enrollment and verification, audio-visual biometric information from an individual is captured, processed and stored as a biometric template. In subsequent uses, biometric information is captured and compared with the biometric templates. If the comparison is successful the verified user could be allowed to sign in to a medical collaboration platform of the hospitals infrastructure. In this work we present the biometric platform developed, the testing methodology and the administrative framework and legal processes, related to GDPR, for the eHealth pilot study and the results from our quantitative and qualitative analysis that was performed.
{"title":"Multi-Channel Biometrics for eHealth Combining Acoustic and Machine Vision Analysis of Speech, Lip Movement and Face: a Case Study","authors":"Emmanouil G Spanakis, D. Takács, A. Karantanas, D. Petrovska-Delacrétaz, Claude Bauzou, Iacob Crucianu, Aymen Mtibaa, Mohamed Amine Hmani, M. Kockmann, Christian Narr, Elnar Hajiyev","doi":"10.1109/IST48021.2019.9010457","DOIUrl":"https://doi.org/10.1109/IST48021.2019.9010457","url":null,"abstract":"The purpose of this work is to present a solution combining user-friendliness and cost-effectiveness use of audio (speech) & visual (video/image) biometrics, for eHealth, able to achieve better accuracy and increase the ability to avoid counterfeiting. This work shows the evaluation results for an eHealth pilot study that tested the security, privacy, usability and cost-effective features of a user authentication platform for the management of sensitive heterogeneous multi-scale medical data (i.e. medical imaging such as MRI/CT scans, physical reports, and laboratory results), through easy acquisition of biometric data via laptops, and tablets equipped with cameras and microphones. Regarding the user enrollment and verification, audio-visual biometric information from an individual is captured, processed and stored as a biometric template. In subsequent uses, biometric information is captured and compared with the biometric templates. If the comparison is successful the verified user could be allowed to sign in to a medical collaboration platform of the hospitals infrastructure. In this work we present the biometric platform developed, the testing methodology and the administrative framework and legal processes, related to GDPR, for the eHealth pilot study and the results from our quantitative and qualitative analysis that was performed.","PeriodicalId":117219,"journal":{"name":"2019 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131811744","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}
Pub Date : 2019-12-01DOI: 10.1109/IST48021.2019.9010096
Jin Zheng, Haocheng Ma, Lihui Peng
In recent years, machine learning has become a hot research area and researchers in the field of electrical capacitance tomography (ECT) have also extended machine learning theory to the solution of ECT image reconstruction problem. In this paper, a deep convolutional neural network is constructed for ECT image reconstruction, which can not only solve the forward problem, but also the inverse problem of ECT. The convolutional network consists of two sub-networks. The sub-network for estimating capacitance from permittivity distribution image is mainly composed of convolutional layers and pooling layers, which is called encoder. The sub-network for reconstructing permittivity distribution image from capacitance is composed of full-connected layers, which is called decoder. Testing results show that the proposed CNN has high capacitance estimation accuracy and high image reconstruction quality, along with good generalization ability.
{"title":"A CNN-Based Image Reconstruction for Electrical Capacitance Tomography","authors":"Jin Zheng, Haocheng Ma, Lihui Peng","doi":"10.1109/IST48021.2019.9010096","DOIUrl":"https://doi.org/10.1109/IST48021.2019.9010096","url":null,"abstract":"In recent years, machine learning has become a hot research area and researchers in the field of electrical capacitance tomography (ECT) have also extended machine learning theory to the solution of ECT image reconstruction problem. In this paper, a deep convolutional neural network is constructed for ECT image reconstruction, which can not only solve the forward problem, but also the inverse problem of ECT. The convolutional network consists of two sub-networks. The sub-network for estimating capacitance from permittivity distribution image is mainly composed of convolutional layers and pooling layers, which is called encoder. The sub-network for reconstructing permittivity distribution image from capacitance is composed of full-connected layers, which is called decoder. Testing results show that the proposed CNN has high capacitance estimation accuracy and high image reconstruction quality, along with good generalization ability.","PeriodicalId":117219,"journal":{"name":"2019 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122115200","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}
Pub Date : 2019-12-01DOI: 10.1109/IST48021.2019.9010151
D. Hu, K. Lu, Yunjie Yang
Data-driven methods are attracting more and more attention in the field of electrical impedance tomography. Many learning-based tomographic algorithms have been presented and investigated in the past few years. However, few related studies pay attention to the symmetrical geometrical structure of tomographic sensors and the possible benefits it may bring to learning-based image reconstruction. Aiming to this, we propose the concept of electrical impedance maps, which can better reflect the nature of geometry of tomographic sensors and have similar properties to images. Then we design a fully convolutional network to build the relationship between electrical impedance maps and conductivity distribution images. The effectiveness and performance of our method is evaluated by both simulation and experimental datasets with different conductivity distribution patterns.
{"title":"Image reconstruction for electrical impedance tomography based on spatial invariant feature maps and convolutional neural network","authors":"D. Hu, K. Lu, Yunjie Yang","doi":"10.1109/IST48021.2019.9010151","DOIUrl":"https://doi.org/10.1109/IST48021.2019.9010151","url":null,"abstract":"Data-driven methods are attracting more and more attention in the field of electrical impedance tomography. Many learning-based tomographic algorithms have been presented and investigated in the past few years. However, few related studies pay attention to the symmetrical geometrical structure of tomographic sensors and the possible benefits it may bring to learning-based image reconstruction. Aiming to this, we propose the concept of electrical impedance maps, which can better reflect the nature of geometry of tomographic sensors and have similar properties to images. Then we design a fully convolutional network to build the relationship between electrical impedance maps and conductivity distribution images. The effectiveness and performance of our method is evaluated by both simulation and experimental datasets with different conductivity distribution patterns.","PeriodicalId":117219,"journal":{"name":"2019 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"22 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115535529","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}
Pub Date : 2019-12-01DOI: 10.1109/IST48021.2019.9010164
Godwin Enemali, Chang Liu, N. Polydorides, H. McCann
Wavelength Modulation Spectroscopy Tomography (WMST) has been widely used for imaging of critical flame parameters, e.g. temperature distribution and species concentration distribution, in harsh environments. To better characterize turbulent flows in practical applications, it is highly desired to improve the temporal resolution of the WMST method. This paper presents a quasi-parallel data acquisition scheme for WMST, which can maintain the temporal response of a fully parallel system with cost-effective hardware implementation. The key idea is to multiplex among the channels on the fast modulated periods within each wavelength scan. The proposed method was validated by four-channeL quasi-parallel data acquisition of the absorption data using a customized FPGA platform. The results show that accurate harmonics information can be extracted simultaneously for each multiplexed channel.
{"title":"A Quasi-Parallel DAQ Scheme for Wavelength Modulation Spectroscopy Tomography","authors":"Godwin Enemali, Chang Liu, N. Polydorides, H. McCann","doi":"10.1109/IST48021.2019.9010164","DOIUrl":"https://doi.org/10.1109/IST48021.2019.9010164","url":null,"abstract":"Wavelength Modulation Spectroscopy Tomography (WMST) has been widely used for imaging of critical flame parameters, e.g. temperature distribution and species concentration distribution, in harsh environments. To better characterize turbulent flows in practical applications, it is highly desired to improve the temporal resolution of the WMST method. This paper presents a quasi-parallel data acquisition scheme for WMST, which can maintain the temporal response of a fully parallel system with cost-effective hardware implementation. The key idea is to multiplex among the channels on the fast modulated periods within each wavelength scan. The proposed method was validated by four-channeL quasi-parallel data acquisition of the absorption data using a customized FPGA platform. The results show that accurate harmonics information can be extracted simultaneously for each multiplexed channel.","PeriodicalId":117219,"journal":{"name":"2019 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130975708","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}
Pub Date : 2019-12-01DOI: 10.1109/IST48021.2019.9010570
Lei Ye, E. Hunsicker, Baihua Li, Diwei Zhou
Fibre tracking is a non-invasive technique based on Diffusion Tensor Imaging (DTI) that provides useful information about biological anatomy and connectivity. In this paper, we propose a new fibre tracking algorithm, named TAS (Tracking by Angle and Similarity), which is able to overcome the shortfalls of existing algorithms by considering not only the main diffusion directions, but also the similarity of diffusion tensors using non-Euclidean distances. Quantitative comparison is carried out through a collection of simulation experiments using statistics of diffusion tensor anisotropy and volume, and tracking errors. Fibre tracking in Corpus Callosum from a healthy human brain dataset is presented.
纤维跟踪是一种基于弥散张量成像(DTI)的非侵入性技术，可提供有关生物解剖和连接的有用信息。在本文中，我们提出了一种新的光纤跟踪算法，称为TAS (tracking by Angle and Similarity)，它不仅考虑了主要的扩散方向，而且考虑了使用非欧几里得距离的扩散张量的相似性，从而克服了现有算法的不足。通过统计扩散张量的各向异性和体积，以及跟踪误差，收集仿真实验进行定量比较。介绍了健康人脑数据集胼胝体的纤维跟踪。
{"title":"Brain Fibre Tracking Improved by Diffusion Tensor Similarity using Non-Euclidean Distances","authors":"Lei Ye, E. Hunsicker, Baihua Li, Diwei Zhou","doi":"10.1109/IST48021.2019.9010570","DOIUrl":"https://doi.org/10.1109/IST48021.2019.9010570","url":null,"abstract":"Fibre tracking is a non-invasive technique based on Diffusion Tensor Imaging (DTI) that provides useful information about biological anatomy and connectivity. In this paper, we propose a new fibre tracking algorithm, named TAS (Tracking by Angle and Similarity), which is able to overcome the shortfalls of existing algorithms by considering not only the main diffusion directions, but also the similarity of diffusion tensors using non-Euclidean distances. Quantitative comparison is carried out through a collection of simulation experiments using statistics of diffusion tensor anisotropy and volume, and tracking errors. Fibre tracking in Corpus Callosum from a healthy human brain dataset is presented.","PeriodicalId":117219,"journal":{"name":"2019 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125430649","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}
Pub Date : 2019-12-01DOI: 10.1109/IST48021.2019.9010210
Nabila Eladawi, Mohammed M Elmogy, M. Ghazal, L. Fraiwan, A. Aboelfetouh, A. Riad, H. Sandhu, A. El-Baz
Diabetic Retinopathy (DR) is considered one of the major reasons for vision loss in the working-age population in most of the countries. DR is caused by high blood sugar levels (diabetes), which damages retinal blood vessels and leads to blindness. Both diagnosis and grading of DR require manual measurements and visual assessment of the changes that happen in the retina, which is a highly complex task. Thus, there is an unmet clinical need for a non-invasive and objective diagnostic system, which can improve the accuracy of both early signs and grading detection for DR. In this paper, we proposed a computer-aided diagnosis (CAD) system for detecting early signs as well as grading of DR. Four significant retinal vasculature features are extracted from optical coherence tomography angiography (OCTA) scans, which reflect the changes in the retinal blood vessels due to DR progress. The developed system fuses these four significant features with clinical and demographic biomarkers. The proposed system uses a 3D convolutional neural network (CNN) to segment blood vessels from both OCTA deep and superficial plexuses. Finally, these extracted features are classified by using the random forest (RF) technique to differentiate first between the DR from normal subjects. Then, grade the DR subjects into mild or moderate. Our preliminary results of grading DR in a cohort of patients (n == 100) demonstrated an average accuracy of 96.8%, sensitivity of 98.1%, and specificity of 88.8%. These results show the feasibility of the proposed approach in early detection as well as the grading of DR.
{"title":"Diabetic Retinopathy Grading Using 3D Multi-path Convolutional Neural Network Based on Fusing Features from OCTA Scans, Demographic, and Clinical Biomarkers","authors":"Nabila Eladawi, Mohammed M Elmogy, M. Ghazal, L. Fraiwan, A. Aboelfetouh, A. Riad, H. Sandhu, A. El-Baz","doi":"10.1109/IST48021.2019.9010210","DOIUrl":"https://doi.org/10.1109/IST48021.2019.9010210","url":null,"abstract":"Diabetic Retinopathy (DR) is considered one of the major reasons for vision loss in the working-age population in most of the countries. DR is caused by high blood sugar levels (diabetes), which damages retinal blood vessels and leads to blindness. Both diagnosis and grading of DR require manual measurements and visual assessment of the changes that happen in the retina, which is a highly complex task. Thus, there is an unmet clinical need for a non-invasive and objective diagnostic system, which can improve the accuracy of both early signs and grading detection for DR. In this paper, we proposed a computer-aided diagnosis (CAD) system for detecting early signs as well as grading of DR. Four significant retinal vasculature features are extracted from optical coherence tomography angiography (OCTA) scans, which reflect the changes in the retinal blood vessels due to DR progress. The developed system fuses these four significant features with clinical and demographic biomarkers. The proposed system uses a 3D convolutional neural network (CNN) to segment blood vessels from both OCTA deep and superficial plexuses. Finally, these extracted features are classified by using the random forest (RF) technique to differentiate first between the DR from normal subjects. Then, grade the DR subjects into mild or moderate. Our preliminary results of grading DR in a cohort of patients (n == 100) demonstrated an average accuracy of 96.8%, sensitivity of 98.1%, and specificity of 88.8%. These results show the feasibility of the proposed approach in early detection as well as the grading of DR.","PeriodicalId":117219,"journal":{"name":"2019 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126728573","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}
Pub Date : 2019-12-01DOI: 10.1109/IST48021.2019.9010108
S. A. Ghaly, M. O. Khan, K. AlMuhanna, K. Al-snaie
An ellipsoidal four-loop MRI coil, comprising four coaxial separately tuned rings on an ellipsoidal surface, is developed and tested. In comparison with spherical four-loop coil which provides a fourth-order homogeneity of the B1 field. This coil aims to provide an effective sixth-order homogeneity of the Bi field while preserving simplicity of design. A complete electrical model of the coil, including all couplings, is investigated to assure these requirements. To facilitate the easy tuning of the developed coil and predict the current-ratio between the centre and outer loops in the co-current mode, an identical self-resonance frequency for all loops is adopted. A prototype shielded elliptical coil at 100,241MHz, consisting in two centre (respectively outer) rings with diameter 4.8 cm (3.4 cm) separated by 1.4cm (4cm), is built. A shielded spherical Coil with the same outer diameter (4.8 cm) was built to permit practical comparisons. Relative measurements of the B1-axial field were carried out in free space. The Ellipsoidal coil (E) exhibits better performances than the Spherical Coil (S). Loaded tests, using water samples of cylindrical shapes, were carried out on a Bruker-Biospec-Avance on MRI images. Axial single-slice (1mm) gradient echo images permitted to provide the axial-field-profile. Quite similar lobe widening is observed for the ellipsoidal coil with regards to the free space results.
{"title":"A Free Element Spherical and Ellipsoidal Radio Frequency Coils with High B1 Homogeneity for MRI","authors":"S. A. Ghaly, M. O. Khan, K. AlMuhanna, K. Al-snaie","doi":"10.1109/IST48021.2019.9010108","DOIUrl":"https://doi.org/10.1109/IST48021.2019.9010108","url":null,"abstract":"An ellipsoidal four-loop MRI coil, comprising four coaxial separately tuned rings on an ellipsoidal surface, is developed and tested. In comparison with spherical four-loop coil which provides a fourth-order homogeneity of the B1 field. This coil aims to provide an effective sixth-order homogeneity of the Bi field while preserving simplicity of design. A complete electrical model of the coil, including all couplings, is investigated to assure these requirements. To facilitate the easy tuning of the developed coil and predict the current-ratio between the centre and outer loops in the co-current mode, an identical self-resonance frequency for all loops is adopted. A prototype shielded elliptical coil at 100,241MHz, consisting in two centre (respectively outer) rings with diameter 4.8 cm (3.4 cm) separated by 1.4cm (4cm), is built. A shielded spherical Coil with the same outer diameter (4.8 cm) was built to permit practical comparisons. Relative measurements of the B1-axial field were carried out in free space. The Ellipsoidal coil (E) exhibits better performances than the Spherical Coil (S). Loaded tests, using water samples of cylindrical shapes, were carried out on a Bruker-Biospec-Avance on MRI images. Axial single-slice (1mm) gradient echo images permitted to provide the axial-field-profile. Quite similar lobe widening is observed for the ellipsoidal coil with regards to the free space results.","PeriodicalId":117219,"journal":{"name":"2019 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114672265","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}
Pub Date : 2019-12-01DOI: 10.1109/IST48021.2019.9010426
M. S. Jabin, F. Magrabi, P. Hibbert, T. Schultz, Taryn Bessen, W. Runciman
Medical imaging health information technology systems, such as Radiology Information Systems and Picture Archiving and Communication Systems were introduced to improve efficiency. Although they have the potential to improve healthcare delivery and patient outcomes, when poorly designed, implemented or managed, they can pose substantial risks to patient safety and organizational efficiency, which may offset the intended benefits. This study used the method of thematic analysis, which provided information about system issues related to health information technology. System issues, including system error, system malfunction or failure, system design, system crash, system functionality, voice recognition technology, launching of new systems, and system integration accounted for 21% of the 436 HIT incidents. Even when these issues do not harm patients, they often cause delays, inconvenience, and inefficiencies. Obtaining the right system, proper and careful system implementation, and immediate back-up systems can improve the safety and quality of care in medical imaging.
{"title":"Identifying and characterizing system issues of health information technology in medical imaging as a basis for recommendations","authors":"M. S. Jabin, F. Magrabi, P. Hibbert, T. Schultz, Taryn Bessen, W. Runciman","doi":"10.1109/IST48021.2019.9010426","DOIUrl":"https://doi.org/10.1109/IST48021.2019.9010426","url":null,"abstract":"Medical imaging health information technology systems, such as Radiology Information Systems and Picture Archiving and Communication Systems were introduced to improve efficiency. Although they have the potential to improve healthcare delivery and patient outcomes, when poorly designed, implemented or managed, they can pose substantial risks to patient safety and organizational efficiency, which may offset the intended benefits. This study used the method of thematic analysis, which provided information about system issues related to health information technology. System issues, including system error, system malfunction or failure, system design, system crash, system functionality, voice recognition technology, launching of new systems, and system integration accounted for 21% of the 436 HIT incidents. Even when these issues do not harm patients, they often cause delays, inconvenience, and inefficiencies. Obtaining the right system, proper and careful system implementation, and immediate back-up systems can improve the safety and quality of care in medical imaging.","PeriodicalId":117219,"journal":{"name":"2019 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"358 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123945016","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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