{"title":"Orthogonal matching pursuit & compressive sampling matching pursuit for Doppler ultrasound signal reconstruction","authors":"S. M. S. Zobly, Y. Kadah","doi":"10.1109/CIBEC.2012.6473336","DOIUrl":null,"url":null,"abstract":"In this work we want to make use of a novel framework of compressed sensing (CS) sampling theory to reconstruct the Doppler ultrasound signal. CS aim to reconstruct signals and images from significantly fewer measurements. Doppler ultrasound is one of the most non-invasive diagnostic techniques. The present data acquisition methods use much data to acquire the image, this cause in increasing the process time and heating. To overcome this limitation we propose a framework of CS. The result shows that the reconstruction performed perfectly with high quality in very short time, by using two CS reconstruction algorithms, orthogonal matching pursuit and compressive sampling matching pursuit algorithms. There is no significant difference in the quality of the resulting images reconstructed by using both reconstruction algorithms.","PeriodicalId":416740,"journal":{"name":"2012 Cairo International Biomedical Engineering Conference (CIBEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Cairo International Biomedical Engineering Conference (CIBEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIBEC.2012.6473336","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14
Abstract
In this work we want to make use of a novel framework of compressed sensing (CS) sampling theory to reconstruct the Doppler ultrasound signal. CS aim to reconstruct signals and images from significantly fewer measurements. Doppler ultrasound is one of the most non-invasive diagnostic techniques. The present data acquisition methods use much data to acquire the image, this cause in increasing the process time and heating. To overcome this limitation we propose a framework of CS. The result shows that the reconstruction performed perfectly with high quality in very short time, by using two CS reconstruction algorithms, orthogonal matching pursuit and compressive sampling matching pursuit algorithms. There is no significant difference in the quality of the resulting images reconstructed by using both reconstruction algorithms.