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A hybrid patient-specific biomechanical model based image registration method for the motion estimation of lungs

机译：基于混合患者特异性生物力学模型的肺部运动估计的图像配准法

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This paper presents a new hybrid biomechanical model-based non-rigid image registration method for lung motion estimation. In the proposed method, a patient-specific biomechanical modelling process captures major physically realistic deformations with explicit physical modelling of sliding motion, whilst a subsequent non-rigid image registration process compensates for small residuals. The proposed algorithm was evaluated with 10 4D CT datasets of lung cancer patients. The target registration error (TRE), defined as the Euclidean distance of landmark pairs, was significantly lower with the proposed method (TRE = 1.37 mm) than with biomechanical modelling (TRE = 3.81 mm) and intensity-based image registration without specific considerations for sliding motion (TRE = 4.57 mm). The proposed method achieved a comparable accuracy as several recently developed intensity-based registration algorithms with sliding handling on the same datasets. A detailed comparison on the distributions of TREs with three non-rigid intensity-based algorithms showed that the proposed method performed especially well on estimating the displacement field of lung surface regions (mean TRE = 1.33 mm, maximum TRE = 5.3 mm). The effects of biomechanical model parameters (such as Poisson's ratio, friction and tissue heterogeneity) on displacement estimation were investigated. The potential of the algorithm in optimising biomechanical models of lungs through analysing the pattern of displacement compensation from the image registration process has also been demonstrated. (C) 2017 Elsevier B.V. All rights reserved.
机译：本文提出了一种新的肺部运动估计基于混合生物力学模型的非刚性图像配准法。在所提出的方法中，患者特异性生物力学建模过程捕获具有显式物理建模的滑动运动的主要物理逼真变形，而随后的非刚性图像配准过程补偿了小的残余物。通过10 4D CT Datasets的肺癌患者进行评价该算法。定义为地标对的欧几里德对的欧几里德对距离的目标登记误差（TRE），与具有生物力学建模（TRE = 3.81mm）和基于强度的图像登记而没有特定考虑的方法显着降低滑动运动（TRE = 4.57 mm）。所提出的方法实现了与最近开发的基于强度的配准算法相当的精度，其具有在同一数据集上滑动处理。对具有三种非刚性强度的算法的TRE分布的详细比较表明，在估计肺表面区域的位移场（平均Tre = 1.33mm，最大Tre = 5.3mm）上尤其良好地进行了尤其良好的方法。研究了生物力学模型参数（如泊松比，摩擦和组织异质性）对位移估计的影响。还证明了通过分析来自图像配准过程的位移补偿模式优化肺生物力学模型的算法的潜力。 （c）2017 Elsevier B.v.保留所有权利。

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