MRF DENOISING WITH COMPRESSED SENSING AND ADAPTIVE FILTERING

被引：0
|
作者：
Wang, Zhe [1 ]
Zhang, Qinwei [2 ]
Yuan, Jing [2 ]
Wang, Xiaogang [1 ]
机构：
[1] Chinese Univ Hong Kong, Dept Elect Engn, Hong Kong, Hong Kong, Peoples R China
[2] Chinese Univ Hong Kong, Dept Imaging & Intervent Radiol, Hong Kong, Hong Kong, Peoples R China
来源：
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) | 2014年
关键词：
Magnetic resonance fingerprinting; compressed sensing; decision tree; bilateral filtering;
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
The recently proposed Magnetic Resonance Fingerprinting (MRF) technique can simultaneously estimate multiple parameters through dictionary matching. It has promising potentials in a wide range of applications. However, MRF introduces errors due to undersampling during the data acquisition process and the limit of dictionary resolution. In this paper, we investigate the error source of MRF and propose the technologies of improving the quality of MRF with compressed sensing, error prediction by decision trees, and adaptive filtering. Experimental results support our observations and show significant improvement of the proposed technologies.
引用
收藏
页码：870 / 873
页数：4
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