{"title":"基于自适应网格分组方法的EIT快速静态图像重建","authors":"K. Cho, E. Woo, Sung Tack Ko","doi":"10.1109/IEMBS.1997.754574","DOIUrl":null,"url":null,"abstract":"For the practical applications of the electrical impedance tomography (EIT) technology, it is essential to reconstruct static images with a higher spatial resolution in a reasonable amount of processing time. Using the conventional EIT static image reconstruction algorithms, however, the processing time increases rapidly with poor convergence characteristics as we try to get a higher spatial resolution. In order to overcome this problem, we propose an adaptive mesh grouping method based on a fuzzy-GA algorithm. Computer simulations using the improved Newton-Raphson method combined with the adaptive mesh grouping algorithm showed a promising result that we can significantly reduce the computation time without sacrificing the spatial resolution.","PeriodicalId":342750,"journal":{"name":"Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Fast Static Image Reconstruction using Adaptive Mesh Grouping Method in EIT\",\"authors\":\"K. Cho, E. Woo, Sung Tack Ko\",\"doi\":\"10.1109/IEMBS.1997.754574\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For the practical applications of the electrical impedance tomography (EIT) technology, it is essential to reconstruct static images with a higher spatial resolution in a reasonable amount of processing time. Using the conventional EIT static image reconstruction algorithms, however, the processing time increases rapidly with poor convergence characteristics as we try to get a higher spatial resolution. In order to overcome this problem, we propose an adaptive mesh grouping method based on a fuzzy-GA algorithm. Computer simulations using the improved Newton-Raphson method combined with the adaptive mesh grouping algorithm showed a promising result that we can significantly reduce the computation time without sacrificing the spatial resolution.\",\"PeriodicalId\":342750,\"journal\":{\"name\":\"Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IEMBS.1997.754574\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEMBS.1997.754574","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fast Static Image Reconstruction using Adaptive Mesh Grouping Method in EIT
For the practical applications of the electrical impedance tomography (EIT) technology, it is essential to reconstruct static images with a higher spatial resolution in a reasonable amount of processing time. Using the conventional EIT static image reconstruction algorithms, however, the processing time increases rapidly with poor convergence characteristics as we try to get a higher spatial resolution. In order to overcome this problem, we propose an adaptive mesh grouping method based on a fuzzy-GA algorithm. Computer simulations using the improved Newton-Raphson method combined with the adaptive mesh grouping algorithm showed a promising result that we can significantly reduce the computation time without sacrificing the spatial resolution.