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, Prance 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, Prance CHU, University Hospital of Rennes, Radiology Dept., F-35043 Rennes, Prance 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： 76230L.1-76230L.9
摘要： Arterial spin labeling (ASL) is a noninvasive MRI method that uses magnetically labeled blood to measure cerebral perfusion. Spatial resolution of ASL is relatively small and as a consequence perfusion from different tissue types is mixed in each pixel. An average ratio of gray matter (GM) to white matter (WM) blood flow is 3 to 1. Disregarding the partial volume effects (PVE) can thus cause serious errors of perfusion quantification. PVE also complicates spatial filtering of ASL images as apart from noise there is a spatial signal variation due to tissue partial volume. Recently, an algorithm for correcting PVE has been published by Asllani et al. It represents the measured magnetization as a sum of different tissue magnetizations weighted by their fractional volume in a pixel. With the knowledge of the partial volume obtained from a high-resolution MRI image, it is possible to separate the individual tissue contributions by linear regression on a neighborhood of each pixel. We propose an extension of this algorithm by minimizing the total-variation of the tissue specific magnetization. This makes the algorithm more flexible to local changes in perfusion. We show that this method can be used to denoise ASL images without mixing the WM and GM signal.
分类号： R312`R312
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