{"title":"利用多重磁共振测量诊断支持","authors":"F. Baselice, V. Pascazio, G. Ferraioli","doi":"10.1109/IWMN.2015.7322982","DOIUrl":null,"url":null,"abstract":"Within this manuscript a new application of Com-pressive Sensing (CS) in Magnetic Resonance Imaging field with the aim of jointly exploit multiple measurements is presented. At the present, in literature CS is exploited in order to allow image formation from a non fully sampled k-space signals, greatly reducing the acquisition time. What we propose is the exploitation of CS for achieving the so called super-resolution, i.e. the possibility of distinguish anatomical structures smaller than the spatial resolution of the image. In particular, the proposed Intra Voxel Analysis (IVA) technique, by combining different acquisition with standard resolution, is able to estimate contributions from different tissues inside the same voxel.","PeriodicalId":440636,"journal":{"name":"2015 IEEE International Workshop on Measurements & Networking (M&N)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The exploitation of multiple MR measurements for diagnosis support\",\"authors\":\"F. Baselice, V. Pascazio, G. Ferraioli\",\"doi\":\"10.1109/IWMN.2015.7322982\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Within this manuscript a new application of Com-pressive Sensing (CS) in Magnetic Resonance Imaging field with the aim of jointly exploit multiple measurements is presented. At the present, in literature CS is exploited in order to allow image formation from a non fully sampled k-space signals, greatly reducing the acquisition time. What we propose is the exploitation of CS for achieving the so called super-resolution, i.e. the possibility of distinguish anatomical structures smaller than the spatial resolution of the image. In particular, the proposed Intra Voxel Analysis (IVA) technique, by combining different acquisition with standard resolution, is able to estimate contributions from different tissues inside the same voxel.\",\"PeriodicalId\":440636,\"journal\":{\"name\":\"2015 IEEE International Workshop on Measurements & Networking (M&N)\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Workshop on Measurements & Networking (M&N)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IWMN.2015.7322982\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Workshop on Measurements & Networking (M&N)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWMN.2015.7322982","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The exploitation of multiple MR measurements for diagnosis support
Within this manuscript a new application of Com-pressive Sensing (CS) in Magnetic Resonance Imaging field with the aim of jointly exploit multiple measurements is presented. At the present, in literature CS is exploited in order to allow image formation from a non fully sampled k-space signals, greatly reducing the acquisition time. What we propose is the exploitation of CS for achieving the so called super-resolution, i.e. the possibility of distinguish anatomical structures smaller than the spatial resolution of the image. In particular, the proposed Intra Voxel Analysis (IVA) technique, by combining different acquisition with standard resolution, is able to estimate contributions from different tissues inside the same voxel.