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Sequential data assimilation for mechanical systems with complex image data: application to tagged-MRI in cardiac mechanics
Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2021-01-09 , DOI: 10.1186/s40323-020-00179-w
Alexandre Imperiale , Dominique Chapelle , Philippe Moireau

Tagged Magnetic Resonance images (tagged-MRI) are generally considered to be the gold standard of medical imaging in cardiology. By imaging spatially-modulated magnetizations of the deforming tissue, indeed, this modality enables an assessment of intra-myocardial deformations over the heart cycle. The objective of the present work is to incorporate the most valuable information contained in tagged-MRI in a data assimilation framework, in order to perform joint state-parameter estimation for a complete biomechanical model of the heart. This type of estimation is the second major step, after initial anatomical personalization, for obtaining a genuinely patient-specific model that integrates the individual characteristics of the patient, an essential prerequisite for benefitting from the model predictive capabilities. Here, we focus our attention on proposing adequate means of quantitatively comparing the cardiac model with various types of data that can be extracted from tagged-MRI after an initial image processing step, namely, 3D displacements fields, deforming tag planes or grids, or apparent 2D displacements. This quantitative comparison—called discrepancy measure—is then used to feed a sequential data assimilation procedure. In the state estimation stage of this procedure, we also propose a new algorithm based on the prediction–correction paradigm, which provides increased flexibility and effectiveness in the solution process. The complete estimation chain is eventually assessed with synthetic data, produced by running a realistic model simulation representing an infarcted heart characterized by increased stiffness and reduced contractility in a given region of the myocardium. From this simulation we extract the 3D displacements, tag planes and grids, and apparent 2D displacements, and we assess the estimation with each corresponding discrepancy measure. We demonstrate that—via regional estimation of the above parameters—the data assimilation procedure allows to quantitatively estimate the biophysical parameters with good accuracy, thus simultaneously providing the location of the infarct and characterizing its seriousness. This shows great potential for combining a biomechanical heart model with tagged-MRI in order to extract valuable new indices in clinical diagnosis.

中文翻译:

具有复杂图像数据的机械系统的顺序数据同化:在心脏力学中应用于标记MRI

标记磁共振图像(标记MRI)通常被认为是心脏病医学影像学的金标准。实际上,通过对变形组织的空间调制磁化强度进行成像,此模态可以评估整个心动周期内的心肌内变形。本工作的目的是将标记的MRI中包含的最有价值的信息纳入数据同化框架中,以便对心脏的完整生物力学模型进行联合状态参数估计。这种类型的估计是继最初的解剖个性化之后的第二个主要步骤,它是获得一个真正的针对特定患者的模型,该模型整合了患者的个体特征,这是受益于模型预测能力的必要前提。这里,我们将注意力集中在提出适当的方法上,以对心脏模型与各种类型的数据进行定量比较,这些数据可以在初始图像处理步骤后从标记MRI中提取,即3D位移场,标签平面或网格变形或明显的2D位移。然后将这种定量比较(称为差异度量)用于提供顺序的数据同化过程。在此过程的状态估计阶段,我们还提出了一种基于预测-校正范例的新算法,该算法在求解过程中提供了更大的灵活性和有效性。最终,将使用综合数据评估完整的估算链,通过运行逼真的模型仿真(代表梗塞的心脏)产生的心肌梗塞,其特征是在给定的心肌区域中增加了刚度并降低了收缩力。从此仿真中,我们提取了3D位移,标记平面和网格以及明显的2D位移,并使用每个相应的差异度量来评估估计。我们证明,通过对上述参数的区域估计,数据同化程序可以定量准确地估计生物物理参数,从而同时提供梗塞的位置并表征其严重性。这显示出将生物力学心脏模型与标记MRI结合以提取临床诊断中有价值的新指标的巨大潜力。标记平面和网格,以及2D视在位移,然后我们使用每个相应的差异度量来评估估计。我们证明,通过对上述参数的区域估计,数据同化程序可以定量准确地估计生物物理参数,从而同时提供梗塞的位置并表征其严重性。这显示出将生物力学心脏模型与标记MRI结合以提取临床诊断中有价值的新指标的巨大潜力。标记平面和网格,以及2D视在位移,然后我们使用每个相应的差异度量来评估估计。我们证明,通过对上述参数的区域估计,数据同化程序可以定量准确地估计生物物理参数,从而同时提供梗塞的位置并表征其严重性。这显示出将生物力学心脏模型与标记MRI结合以提取临床诊断中有价值的新指标的巨大潜力。因此,可以同时提供梗塞的位置并表征其严重性。这显示出将生物力学心脏模型与标记MRI结合以提取临床诊断中有价值的新指标的巨大潜力。因此,可以同时提供梗塞的位置并表征其严重性。这显示出将生物力学心脏模型与标记MRI结合以提取临床诊断中有价值的新指标的巨大潜力。
更新日期:2021-01-12
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