文献类型： 会议论文
第一作者： Jan Petr
作者： Jan Petr 1 ; Jean-Christophe Ferre 2 ; Jean-Yves Gauvrit 2 ; Christian Barillot 1 ;
作者机构： 1.INRIA, VisAGeS Project-Team, F-35042 Rennes, France University of Rennes I, CNRS, UMR 6074, IRISA, F-35042 Rennes, France INSERM, U746, F-35042 Rennes, France
2.INRIA, VisAGeS Project-Team, F-35042 Rennes, France CHU, University Hospital of Rennes, Radiology Dept., F-35043 Rennes, France University of Rennes I, CNRS, UMR 6074, IRISA, F-35042 Rennes, France INSERM, U746, F-35042 Rennes, France
会议名称： Conference on image processing
主办单位：
页码： 76233B.1-76233B.9
摘要： Arterial spin labeling (ASL) is an MRI method for imaging brain perfusion by magnetically labeling blood in brain feeding arteries. The perfusion is obtained from the difference between images with and without prior labeling. Image noise is one of the main problems of ASL as the difference is around 0.5-2% of the image magnitude. Usually, 20-40 pairs of images need to be acquired and averaged to reach a satisfactory quality. The images are acquired shortly after the labeling to allow the labeled blood to reach the imaged slice. A sequence of images with multiple delays is more suitable for quantification of the cerebral blood flow as it gives more information about the blood arrival and relaxation. Although the quantification methods are sensitive to noise, no filtering or only Gaussian filtering is used to denoise the data in the temporal domain prior to quantification. In this article, we propose an efficient way to use the redundancy of information in the time sequence of each pixel to suppress noise. For this purpose, the vectorial NL-means method is adapted to work in the temporal domain. The proposed method is tested on simulated and real 3T MRI data. We demonstrate a clear improvement of the image quality as well as a better performance compared to Gaussian and normal spatial NL-means filtering.
分类号： R312`R312
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