PDF(Pubmed)
Abstract：
Resting cerebral perfusion metrics can be calculated from the MRI ΔR2* signal during the first passage of an intravascular bolus of a Gadolinium-based contrast agent (GBCA), or more recently, a transient hypoxia-induced change in the concentration of deoxyhemoglobin ([dOHb]). Conventional analysis follows a proxy process that includes deconvolution of an arterial input function (AIF) in a tracer kinetic model. We hypothesized that the step reduction in magnetic susceptibility accompanying a step decrease in [dOHb] that occurs when a single breath of oxygen terminates a brief episode of lung hypoxia permits direct calculation of relative perfusion metrics. The time course of the ΔR2* signal response enables both the discrimination of blood arrival times and the time course of voxel filling. We calculated the perfusion metrics implied by this step signal change in seven healthy volunteers and compared them to those from conventional analyses of GBCA and dOHb using their AIF and indicator dilution theory. Voxel-wise maps of relative cerebral blood flow and relative cerebral blood volume had a high spatial and magnitude congruence for all three analyses (r > 0.9) and were similar in appearance to published maps. The mean (SD) transit times (s) in grey and white matter respectively for the step response (7.4 (1.1), 8.05 (1.71)) were greater than those for GBCA (2.6 (0.45), 3.54 (0.83)) attributable to the nature of their respective calculation models. In conclusion we believe these calculations of perfusion metrics derived directly from ΔR2* have superior merit to calculations via AIF by virtue of being calculated from a direct signal rather than through a proxy model which encompasses errors inherent in designating an AIF and performing deconvolution calculations.
摘要：
静息脑灌注指标可以从MRIΔR2*信号计算在基于钆的造影剂（GBCA）的血管内推注的第一次通过期间，或者最近，短暂的缺氧诱导的脱氧血红蛋白浓度变化（[dOHb]）。常规分析遵循代理过程，该过程包括在示踪剂动力学模型中对动脉输入函数（AIF）进行反卷积。我们假设，当一次氧气呼吸终止短暂的肺缺氧发作时，磁化率的逐步降低伴随着[dOHb]的逐步降低，可以直接计算相对灌注指标。ΔR2*信号响应的时间过程能够区分血液到达时间和体素填充的时间过程。我们计算了七名健康志愿者的这一阶跃信号变化所暗示的灌注指标，并使用他们的AIF和指标稀释理论将其与GBCA和dOHb的常规分析进行了比较。对于所有三种分析（r>0.9），相对脑血流量和相对脑血容量的体素图均具有很高的空间和幅度一致性，并且外观与已发布的图相似。阶跃反应的灰质和白质平均(SD)渡越时间(s)(7.4(1.1)，8.05(1.71))高于GBCA(2.6(0.45)，3.54(0.83))归因于其各自计算模型的性质。总之,我们相信这些直接从ΔR2*导出的灌注度量的计算具有优于经由AIF的计算的优点,这是因为它们是从直接信号而不是通过代理模型来计算的,该代理模型包含在指定AIF和执行反卷积计算中固有的误差。
