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Deconvolution-Based CT and MR Brain Perfusion Measurement: Theoretical Model Revisited and Practical Implementation Details

机译:基于反卷积的CT和MR脑灌注测量:重新研究理论模型和实际实现细节

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摘要

Deconvolution-based analysis of CT and MR brain perfusion data is widely used in clinical practice and it is still a topic of ongoing research activities. In this paper, we present a comprehensive derivation and explanation of the underlying physiological model for intravascular tracer systems. We also discuss practical details that are needed to properly implement algorithms for perfusion analysis. Our description of the practical computer implementation is focused on the most frequently employed algebraic deconvolution methods based on the singular value decomposition. In particular, we further discuss the need for regularization in order to obtain physiologically reasonable results. We include an overview of relevant preprocessing steps and provide numerous references to the literature. We cover both CT and MR brain perfusion imaging in this paper because they share many common aspects. The combination of both the theoretical as well as the practical aspects of perfusion analysis explicitly emphasizes the simplifications to the underlying physiological model that are necessary in order to apply it to measured data acquired with current CT and MR scanners.
机译:基于反卷积的CT和MR脑灌注数据分析在临床实践中被广泛使用,它仍然是正在进行的研究活动的主题。在本文中,我们对血管内示踪剂系统的基本生理模型进行了全面的推导和解释。我们还将讨论正确实现灌注分析算法所需的实用细节。我们对实际计算机实现的描述集中于基于奇异值分解的最常用代数反卷积方法。特别是,我们进一步讨论了进行正则化的需求,以获得生理上合理的结果。我们提供了有关预处理步骤的概述,并提供了大量参考文献。本文涵盖了CT和MR脑灌注成像,因为它们具有许多共同的方面。灌注分析的理论和实践方面的结合明确强调了对基础生理模型的简化,这是将其应用于当前CT和MR扫描仪采集的测量数据所必需的。

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