Chappell Michael A, Okell Thomas W, Jezzard Peter, Woolrich Mark W
Oxford Centre for Functional MRI of the Brain, University of Oxford, John Radcliffe Hospital, Headington, Oxford, OX4 3HD, UK.
Med Image Comput Comput Assist Interv. 2009;12(Pt 2):514-21. doi: 10.1007/978-3-642-04271-3_63.
Arterial Spin Labeling (ASL) permits the non-invasive assessment of cerebral perfusion, by magnetically labeling all the blood flowing into the brain. Vessel encoded (VE) ASL extends this concept by introducing spatial modulations of the labeling procedure, resulting in different patterns of label applied to the blood from different vessels. Here a Bayesian inference solution to the analysis of VE-ASL is presented based on a description of the relative locations of labeled vessels and a probabilistic classification of brain tissue to vessel source. In simulation and on real data the method is shown to reliably determine vascular territories in the brain, including the case where the number of vessels exceeds the number of independent measurements.
动脉自旋标记(ASL)通过对流入大脑的所有血液进行磁性标记,实现对脑灌注的无创评估。血管编码(VE)ASL通过引入标记过程的空间调制扩展了这一概念,从而使来自不同血管的血液具有不同的标记模式。本文基于标记血管的相对位置描述和脑组织到血管来源的概率分类,提出了一种用于分析VE-ASL的贝叶斯推理解决方案。在模拟和实际数据中,该方法被证明能够可靠地确定大脑中的血管区域,包括血管数量超过独立测量数量的情况。