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A novel approach to quantification of real and artifactual components of current density imaging for phantom and live heart

机译:一种新的定量密度成像对幽灵和活心脏成像的真实和艺术分量的新方法

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Spatial distribution of injected current in a subject could be calculated and visualized through current density imaging (CDI). Calculated CDI paths however have a limited degree of accuracy due to both avoidable methodological errors and inevitable limitations dictated by MR imaging constraints. The source and impact of these limitations are scrutinized in this paper. Quantification of such limitations is an essential step prior to passing any judgment about the results especially in biomedical applications. An innovative technique along with metrics for evaluation of range of errors using baseline and phase cycle MR images is proposed in this work. The presented approach is helpful in pinpointing the local artifacts (areas for which CDI results are suspect), evaluation of global noises and artifacts and assessment of the effect of approximation algorithms on real and artifactual components. We will demonstrate how this error/reliability evaluation is applicable to interpretation of CDI results and in this framework, report the CDI results for an artificial phantom and a live pig heart in Langendorff setup. It is contended here that using this method, the inevitable trade-off between details and approximations of CDI components could be monitored which provides a great opportunity for robust interpretation of results. The proposed approach could be extended, adapted and used for statistical analysis of similar methods which aim at mapping current and impedance based on magnetic flux images obtained through MRI.
机译:可以通过电流密度成像(CDI)来计算受试者中注射电流的空间分布。然而,由于避免的方法误差和MR成像约束所示的不可避免的局限性,所计算的CDI路径具有有限的准确度。本文审查了这些限制的来源和影响。在通过任何关于生物医学应用的结果之前,这些限制的定量是必不可少的步骤。在这项工作中提出了一种创新技术以及用于评估使用基线和阶段周期MR图像的误差范围的度量。所提出的方法有助于查明当地工件(CDI结果是可疑的区域),对全球噪声和伪影的评估以及评估近似算法对真实和艺术组件的影响。我们将展示该错误/可靠性评估如何适用于CDI结果和本框架的解释,向Langendorff设置中的人工幻影和活猪心脏报告CDI结果。这里涉及使用这种方法,可以监测CDI组件的细节和近似之间的不可避免的权衡,这提供了对结果的强大解释的绝佳机会。可以扩展所提出的方法,适应并用于统计分析类似方法的统计分析,该方法旨在基于通过MRI获得的磁通量图像映射电流和阻抗。

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