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基于边界元法的左心室变形恢复正则化方法

Boundary element method-based regularization for recovering of LV deformation.

作者信息

Yan Ping, Sinusas Albert, Duncan James S

机构信息

Biomedical Engineering, Radiology and Medicine Departments, Yale University, New Haven, CT 06520, USA.

出版信息

Med Image Anal. 2007 Dec;11(6):540-54. doi: 10.1016/j.media.2007.04.007. Epub 2007 May 22.

Abstract

The quantification of left ventricular (LV) deformation from noninvasive image sequences is an important clinical problem. To date, feature information from either magnetic resonance (MR), computed tomographic (CT) or echocardiographic image data have been assembled with the help of different regularization models to estimate LV deformation. The currently available regularization models have tradeoffs related to accuracy, lattice density, physical plausibility and computation time. This paper introduces a new regularization model based on the boundary element method (BEM) which can overcome these tradeoffs. We then employ this new regularization model with the generalized robust point matching (GRPM) strategy to estimate the dense displacement fields and strains from 3D LV image sequences. The approach is evaluated on in vivo cardiac magnetic resonance image sequences. All results are compared to displacements found using implanted markers, taken to be a gold standard. The approach is also evaluated on the 4D real time echocardiographic image sequences and the results demonstrate that the approach is capable of tracking the LV deformation for echocardiography.

摘要

从无创图像序列中量化左心室(LV)变形是一个重要的临床问题。迄今为止,磁共振(MR)、计算机断层扫描(CT)或超声心动图图像数据的特征信息已借助不同的正则化模型进行整合,以估计LV变形。目前可用的正则化模型在准确性、晶格密度、物理合理性和计算时间方面存在权衡。本文介绍了一种基于边界元法(BEM)的新正则化模型,该模型可以克服这些权衡。然后,我们将这种新的正则化模型与广义鲁棒点匹配(GRPM)策略相结合,以从3D LV图像序列中估计密集位移场和应变。该方法在体内心脏磁共振图像序列上进行了评估。所有结果均与使用植入标记物获得的位移进行比较,植入标记物的位移被视为金标准。该方法还在4D实时超声心动图图像序列上进行了评估,结果表明该方法能够跟踪超声心动图中的LV变形。

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