A new framework for assessing subject-specific whole brain circulation and perfusion using MRI-based measurements and a multi-scale continuous flow model
A new framework for assessing subject-specific whole brain circulation and perfusion using MRI-based measurements and a multi-scale continuous flow model
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DOI：
10.1371/journal.pcbi.1007073
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发表时间：
2019-06-01
影响因子：
4.3
通讯作者：
Nordbotten, Jan M.
中科院分区：
文献类型：
--
作者：
Hodneland, Erlend;Hanson, Erik;Nordbotten, Jan M.
各种各样的严重的医疗条件涉及微血管循环的改变。因此，循环和灌注的测量或模拟具有相当大的临床价值，并且可以用于诊断、治疗功效的评估以及用于手术规划。然而，传统的示踪动力学一室模型的准确性是有限的，由于规模依赖性。作为一种补救措施，我们提出了一个规模不变的数学框架来模拟全脑灌注。建议的框架是基于解剖几何形状的分割成像体素分辨率。动脉和静脉网络中的大血管通过飞行时间（ToF）和定量磁化率映射（QSM）识别。大血管网络中的宏观尺度流动是准确建模使用哈根-Poietille方程，而毛细管流被视为两室多孔介质流。宏观尺度流与微观尺度流通过末端空间分布的支持函数耦合。灌注被定义为液体从动脉室到静脉室的过渡。我们展示了一个全脑模拟示踪剂传播的一个现实的几何模型的人脑，该模型包括不同的灰色和白色物质，以及大血管的动脉和静脉血管网络。我们提出的框架是一个准确的和可行的替代传统的房室模型，具有高相关性的模拟脑灌注，也为恢复现场参数在临床brain perfusion application.Author摘要一个准确的模拟在人脑中的血流可以用于改善诊断和分配个性化的治疗方案。然而，目前的算法仅限于模拟肿瘤内的血流，并且在参数估计方面，传统的隔室模型由于模型内缺乏空间连通性而具有有限的准确性。作为一种补救措施，我们提出了一个数据驱动的计算流体动力学模型，其中模拟的几何域是从最先进的MR采集中定义的，可以分割大动脉和静脉。在毛细血管组织中，我们应用一个两室多孔介质模型，其中灌注是压力驱动的，并被定义为血液从动脉侧到静脉侧的过渡。此外，我们提出了一个模型，用于处理中间规模的问题，其中的船只是不可检测的，流量不遵守多孔介质流动的要求。对于这个规模，我们提出了一个支持函数分布在附近区域的船舶码头周围的流体。结合这些元素，我们开发了一种新的全人脑血流模拟器。
A large variety of severe medical conditions involve alterations in microvascular circulation. Hence, measurements or simulation of circulation and perfusion has considerable clinical value and can be used for diagnostics, evaluation of treatment efficacy, and for surgical planning. However, the accuracy of traditional tracer kinetic one-compartment models is limited due to scale dependency. As a remedy, we propose a scale invariant mathematical framework for simulating whole brain perfusion. The suggested framework is based on a segmentation of anatomical geometry down to imaging voxel resolution. Large vessels in the arterial and venous network are identified from time-of-flight (ToF) and quantitative susceptibility mapping (QSM). Macro-scale flow in the large-vessel-network is accurately modelled using the Hagen-Poiseuille equation, whereas capillary flow is treated as two-compartment porous media flow. Macro-scale flow is coupled with micro-scale flow by a spatially distributing support function in the terminal endings. Perfusion is defined as the transition of fluid from the arterial to the venous compartment. We demonstrate a whole brain simulation of tracer propagation on a realistic geometric model of the human brain, where the model comprises distinct areas of grey and white matter, as well as large vessels in the arterial and venous vascular network. Our proposed framework is an accurate and viable alternative to traditional compartment models, with high relevance for simulation of brain perfusion and also for restoration of field parameters in clinical brain perfusion applications.Author summary An accurate simulation of blood-flow in the human brain can be used for improved diagnostics and assignment of personalized treatment regimes. However, current algorithms are limited to simulation of blood flow within tumours only, and in terms of parameter estimation, traditional compartment models have limited accuracy due to lack of spatial connectivity within the models. As a remedy, we propose a data-driven computational fluid dynamics model where the geometric domains for simulation are defined from state-of-the art MR acquisitions enabling a segmentation of large arteries and veins. In the capillary tissue we apply a two-compartment porous media model, where the perfusion is pressure-driven and is defined as the transition of blood from arterial to venous side. In addition, we propose a model for dealing with the intermediate scale problem where the vessels are undetectable and the flow does not adhere to requirements of porous media flow. For this scale, we propose a support function distributing the fluid in a nearby region around the vessel terminals. Combining these elements, we have developed a novel full human brain blood-flow simulator.