Estimation of tissue contractility from cardiac cine-MRI using a biomechanical heart model
Estimation of tissue contractility from cardiac cine-MRI using a biomechanical heart model
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DOI：
10.1007/s10237-011-0337-8
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发表时间：
2012-05-01
影响因子：
3.5
通讯作者：
Chapelle, D.
中科院分区：
文献类型：
--
作者：
Chabiniok, R.;Moireau, P.;Chapelle, D.
本文的目的是提出和评估的估计程序的基础上，数据同化的原则，非常适合于获得一些区域值的关键生物物理参数在跳动的心脏模型，使用实际的电影磁共振图像。动机有两个方面：（1）提供用于个性化心脏模型的特征的自动工具，以便在患者特异性建模中实现预测性，以及（2）在估计量本身中获得用于诊断目的的一些有用信息。为了评估全球的方法，我们专门设计了一个动物实验中，一个受控的梗死是生产和梗死前后获得的数据，估计区域组织收缩性-一个关键参数直接影响的病理-进行每个测量阶段。在使用合成数据对我们提出的方法进行初步评估后，我们首先根据AHA细分估计与6个区域相关的收缩力值，然后使用实际AHA片段进行更详细的估计，从而展示了全面的应用。通过与特定梗死的医学知识和晚期增强MR图像进行比较来评估估计结果。我们讨论了他们的准确性，在不同的细分水平，根据固有的建模的局限性和内在的信息内容的数据。
The objective of this paper is to propose and assess an estimation procedure-based on data assimilation principles-well suited to obtain some regional values of key biophysical parameters in a beating heart model, using actual Cine-MR images. The motivation is twofold: (1) to provide an automatic tool for personalizing the characteristics of a cardiac model in order to achieve predictivity in patient-specific modeling and (2) to obtain some useful information for diagnosis purposes in the estimated quantities themselves. In order to assess the global methodology, we specifically devised an animal experiment in which a controlled infarct was produced and data acquired before and after infarction, with an estimation of regional tissue contractility-a key parameter directly affected by the pathology-performed for every measured stage. After performing a preliminary assessment of our proposed methodology using synthetic data, we then demonstrate a full-scale application by first estimating contractility values associated with 6 regions based on the AHA subdivision, before running a more detailed estimation using the actual AHA segments. The estimation results are assessed by comparison with the medical knowledge of the specific infarct, and with late enhancement MR images. We discuss their accuracy at the various subdivision levels, in the light of the inherent modeling limitations and of the intrinsic information contents featured in the data.