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On the Validation of a Multiple-Network Poroelastic Model Using Arterial Spin Labeling MRI Data

机译:基于动脉自旋标记MRI数据的多网络多孔弹性模型的验证

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摘要

The Multiple-Network Poroelastic Theory (MPET) is a numerical model to characterize the transport of multiple fluid networks in the brain, which overcomes the problem of conducting separate analyses on individual fluid compartments and losing the interactions between tissue and fluids, in addition to the interaction between the different fluids themselves. In this paper, the blood perfusion results from MPET modeling are partially validated using cerebral blood flow (CBF) data obtained from arterial spin labeling (ASL) magnetic resonance imaging (MRI), which uses arterial blood water as an endogenous tracer to measure CBF. Two subjects—one healthy control and one patient with unilateral middle cerebral artery (MCA) stenosis are included in the validation test. The comparison shows several similarities between CBF data from ASL and blood perfusion results from MPET modeling, such as higher blood perfusion in the gray matter than in the white matter, higher perfusion in the periventricular region for both the healthy control and the patient, and asymmetric distribution of blood perfusion for the patient. Although the partial validation is mainly conducted in a qualitative way, it is one important step toward the full validation of the MPET model, which has the potential to be used as a testing bed for hypotheses and new theories in neuroscience research.
机译:多元网络多孔弹性理论(MPET)是一个数值模型,用于表征大脑中多个流体网络的传输,除了克服了对单个流体隔室进行单独分析并丢失组织与流体之间的相互作用的问题。不同流体本身之间的相互作用。在本文中,使用从动脉自旋标记(ASL)磁共振成像(MRI)获得的脑血流(CBF)数据部分验证了MPET建模的血液灌注结果,该数据使用动脉血水作为内源示踪剂来测量CBF。验证测试包括两名受试者,即一名健康对照者和一名患有单侧大脑中动脉(MCA)狭窄的患者。比较结果显示,ASL的CBF数据与MPET建模的血液灌注结果之间存在一些相似之处,例如灰质的血液灌注高于白质,健康对照和患者的脑室周围血流灌注更高,并且不对称为病人分配血液。尽管部分验证主要以定性方式进行,但这是迈向MPET模型全面验证的重要一步,该模型有可能被用作神经科学研究中的假设和新理论的试验床。

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