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MR Vascular Fingerprinting: A New Approach to Compute Cerebral Blood Volume Mean Vessel Radius and Oxygenation Maps in the Human Brain

机译:MR血管指纹:一种计算人脑中脑血容量平均血管半径和氧合图的新方法

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摘要

In the present study, we describe a fingerprinting approach to analyze the time evolution of the MR signal and retrieve quantitative information about the microvascular network. We used a Gradient Echo Sampling of the Free Induction Decay and Spin Echo (GESFIDE) sequence and defined a fingerprint as the ratio of signals acquired pre and post injection of an iron based contrast agent. We then simulated the same experiment with an advanced numerical tool that takes a virtual voxel containing blood vessels as input, then computes microscopic magnetic fields and water diffusion effects, and eventually derives the expected MR signal evolution. The parameters inputs of the simulations (cerebral blood volume [CBV], mean vessel radius [R], and blood oxygen saturation [SO2]) were varied to obtain a dictionary of all possible signal evolutions. The best fit between the observed fingerprint and the dictionary was then determined using least square minimization. This approach was evaluated in 5 normal subjects and the results were compared to those obtained using more conventional MR methods, steady-state contrast imaging for CBV and R and a global measure of oxygenation obtained from the superior sagittal sinus for SO2. The fingerprinting method enabled the creation of high-resolution parametric maps of the microvascular network showing expected contrast and fine details. Numerical values in gray matter (CBV=3.1±0.7%, R=12.6±2.4µm, SO2=59.5±4.7%) are consistent with literature reports and correlated with conventional MR approaches. SO2 values in white matter (53.0±4.0%) were slightly lower than expected. Numerous improvements can easily be made and the method should be useful to study brain pathologies.
机译:在本研究中,我们描述了一种指纹分析方法,用于分析MR信号的时间演变并检索有关微血管网络的定量信息。我们使用了自由感应衰减和自旋回波(GESFIDE)序列的梯度回波采样,并将指纹定义为铁基造影剂注射前后注射的信号比率。然后,我们使用高级数值工具模拟了相同的实验,该工具以包含血管的虚拟体素作为输入,然后计算微观磁场和水扩散效应,并最终得出预期的MR信号演变。模拟的参数输入(脑血容量[CBV],平均血管半径[R]和血氧饱和度[SO2])被改变以获得所有可能信号演变的字典。然后使用最小二乘最小化确定观察到的指纹与字典之间的最佳拟合。在5名正常受试者中评估了该方法,并将结果与​​使用更常规的MR方法,CBV和R的稳态对比成像以及从上矢状窦对SO2获得的总体氧合测量结果进行了比较。指纹方法可以创建微血管网络的高分辨率参数图,从而显示出预期的对比度和精细的细节。灰质的数值(CBV = 3.1±0.7%,R = 12.6±2.4μm,SO2 = 59.5±4.7%)与文献报道一致,并且与常规MR方法相关。白质中的SO2值(53.0±4.0%)略低于预期。可以轻松地进行许多改进,该方法对于研究脑部病理学应该是有用的。

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