{"title":"非均匀介质中非侵入性温度估计的NARX结构","authors":"C. Teixeira, W. Pereira, A. Ruano, M. Ruano","doi":"10.1109/WISP.2007.4447544","DOIUrl":null,"url":null,"abstract":"The safe and effective application of thermal therapies are limited by the existence of precise non-invasive temperature estimators. Such estimators would enable a correct power deposition on the region of interest by means of a correct instrumentation control. In multi-layered media, the temperature should be estimated at each layer and especially at the interfaces, where significant temperature changes should occur during therapy. In this work, a non-linear autoregressive structure with exogenous inputs (NARX) was applied to non-invasively estimate temperature in a multi-layered (non-homogeneous) medium, while submitted to physiotherapeutic ultrasound. The NARX structure is composed by a static feed-forward radial basis functions neural network (RBFNN), with external dynamics induced by its inputs. The NARX structure parameters were optimized by means of a multi-objective genetic algorithm. The best attained models reached a maximum absolute error inferior to 0.5degC (proposed threshold in hyperthermia/diathermia) at both the interface and inner layer points, at four radiation intensities. These models present also a small computational complexity as desired for real-time applications. To the best of ours knowledge this is the first non-invasive estimation approach in multi-layered media using ultrasound for both heating and estimation.","PeriodicalId":164902,"journal":{"name":"2007 IEEE International Symposium on Intelligent Signal Processing","volume":"79 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"NARX structures for non-invasive temperature estimation in non-homogeneous media\",\"authors\":\"C. Teixeira, W. Pereira, A. Ruano, M. Ruano\",\"doi\":\"10.1109/WISP.2007.4447544\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The safe and effective application of thermal therapies are limited by the existence of precise non-invasive temperature estimators. Such estimators would enable a correct power deposition on the region of interest by means of a correct instrumentation control. In multi-layered media, the temperature should be estimated at each layer and especially at the interfaces, where significant temperature changes should occur during therapy. In this work, a non-linear autoregressive structure with exogenous inputs (NARX) was applied to non-invasively estimate temperature in a multi-layered (non-homogeneous) medium, while submitted to physiotherapeutic ultrasound. The NARX structure is composed by a static feed-forward radial basis functions neural network (RBFNN), with external dynamics induced by its inputs. The NARX structure parameters were optimized by means of a multi-objective genetic algorithm. The best attained models reached a maximum absolute error inferior to 0.5degC (proposed threshold in hyperthermia/diathermia) at both the interface and inner layer points, at four radiation intensities. These models present also a small computational complexity as desired for real-time applications. To the best of ours knowledge this is the first non-invasive estimation approach in multi-layered media using ultrasound for both heating and estimation.\",\"PeriodicalId\":164902,\"journal\":{\"name\":\"2007 IEEE International Symposium on Intelligent Signal Processing\",\"volume\":\"79 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE International Symposium on Intelligent Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WISP.2007.4447544\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE International Symposium on Intelligent Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WISP.2007.4447544","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
NARX structures for non-invasive temperature estimation in non-homogeneous media
The safe and effective application of thermal therapies are limited by the existence of precise non-invasive temperature estimators. Such estimators would enable a correct power deposition on the region of interest by means of a correct instrumentation control. In multi-layered media, the temperature should be estimated at each layer and especially at the interfaces, where significant temperature changes should occur during therapy. In this work, a non-linear autoregressive structure with exogenous inputs (NARX) was applied to non-invasively estimate temperature in a multi-layered (non-homogeneous) medium, while submitted to physiotherapeutic ultrasound. The NARX structure is composed by a static feed-forward radial basis functions neural network (RBFNN), with external dynamics induced by its inputs. The NARX structure parameters were optimized by means of a multi-objective genetic algorithm. The best attained models reached a maximum absolute error inferior to 0.5degC (proposed threshold in hyperthermia/diathermia) at both the interface and inner layer points, at four radiation intensities. These models present also a small computational complexity as desired for real-time applications. To the best of ours knowledge this is the first non-invasive estimation approach in multi-layered media using ultrasound for both heating and estimation.