{"title":"基于模拟-信息转换的压缩超声成像","authors":"Yi Lv, Wen-tao Wu","doi":"10.1109/ICBEB.2012.110","DOIUrl":null,"url":null,"abstract":"The paper proposes a compressive sensing method adapted to ultrasound imaging, following the recently developed Analog to Information (AIC) framework. First, basis functions in time domain based on waveform were constructed to achieve sparse signal representation. Second, utilizing the basis functions and using AIC framework, a system is designed with lowered the sampling rates and reduced the total data size of ultrasound imaging. The results show that in the experiment the sampling rate below Nyquist frequency and only 30% amount of data are used to implement ultrasound imaging without reducing the quality of image. The sampling rate is lowered and the amount of data is reduced greatly by the proposed method based on compressive sensing.","PeriodicalId":6374,"journal":{"name":"2012 International Conference on Biomedical Engineering and Biotechnology","volume":"18 1","pages":"758-761"},"PeriodicalIF":0.0000,"publicationDate":"2012-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Compressive Ultrasound Imaging Based on Analog to Information Conversion\",\"authors\":\"Yi Lv, Wen-tao Wu\",\"doi\":\"10.1109/ICBEB.2012.110\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper proposes a compressive sensing method adapted to ultrasound imaging, following the recently developed Analog to Information (AIC) framework. First, basis functions in time domain based on waveform were constructed to achieve sparse signal representation. Second, utilizing the basis functions and using AIC framework, a system is designed with lowered the sampling rates and reduced the total data size of ultrasound imaging. The results show that in the experiment the sampling rate below Nyquist frequency and only 30% amount of data are used to implement ultrasound imaging without reducing the quality of image. The sampling rate is lowered and the amount of data is reduced greatly by the proposed method based on compressive sensing.\",\"PeriodicalId\":6374,\"journal\":{\"name\":\"2012 International Conference on Biomedical Engineering and Biotechnology\",\"volume\":\"18 1\",\"pages\":\"758-761\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-05-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 International Conference on Biomedical Engineering and Biotechnology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICBEB.2012.110\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Conference on Biomedical Engineering and Biotechnology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICBEB.2012.110","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Compressive Ultrasound Imaging Based on Analog to Information Conversion
The paper proposes a compressive sensing method adapted to ultrasound imaging, following the recently developed Analog to Information (AIC) framework. First, basis functions in time domain based on waveform were constructed to achieve sparse signal representation. Second, utilizing the basis functions and using AIC framework, a system is designed with lowered the sampling rates and reduced the total data size of ultrasound imaging. The results show that in the experiment the sampling rate below Nyquist frequency and only 30% amount of data are used to implement ultrasound imaging without reducing the quality of image. The sampling rate is lowered and the amount of data is reduced greatly by the proposed method based on compressive sensing.