Deformable cardiac surface tracking by adaptive estimation algorithms.
Deformable cardiac surface tracking by adaptive estimation algorithms.
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DOI：
10.1038/s41598-023-28578-0
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发表时间：
2023-01-25
影响因子：
4.6
通讯作者：
中科院分区：
文献类型：
--
作者：
本研究提出了一种基于粒子滤波的框架来从单个磁共振成像(MRI)切片的时间序列中跟踪心脏表面，未来的目标是利用所提出的框架来进行介入性心血管磁共振操作，这依赖于从MRI数据对心脏表面的准确和在线跟踪。该框架利用了心脏表面的低阶参数可变形模型。随机动力学系统表示心脏表面运动。在控制变形程度之前，采用可变形模型来引入形状。在系统的动态模型中，使用自适应滤波器对复杂的心脏运动进行建模。粒子过滤器被用来递归地估计随着时间的推移系统的当前状态。将该方法应用于双室畸形的恢复，并用一个数值模型和多个真实的心脏MRI数据集进行了验证。该算法在每个时间步长分别使用固定和变化的图像切片平面进行多次实验。对于真实的心脏MRI数据集，对于固定和变化的图像切片平面，平均均方根跟踪误差分别为2.61 mm和3.42 mm。这项工作是一项概念验证研究，通过低阶概率模型来建模和跟踪心脏表面变形，未来的目标是在MR图像引导下将该方法用于有针对性的介入性心脏手术。对于真实的心脏MRI数据集，该方法能够在3个像素的精度内跟踪位于心脏表面不同部分的兴趣点。分析表明，可变形心脏表面跟踪算法的使用为在MRI引导下进行精确的心内靶向消融手术铺平了道路。这项工作的主要贡献是双重的。首先，提出了一种从单个图像切片的时间序列中跟踪整个心脏表面的框架。其次，它使用自适应滤波器将运动信息合并到非刚性心脏表面运动的跟踪中，以实现时间相关性。
This study presents a particle filter based framework to track cardiac surface from a time sequence of single magnetic resonance imaging (MRI) slices with the future goal of utilizing the presented framework for interventional cardiovascular magnetic resonance procedures, which rely on the accurate and online tracking of the cardiac surface from MRI data. The framework exploits a low-order parametric deformable model of the cardiac surface. A stochastic dynamic system represents the cardiac surface motion. Deformable models are employed to introduce shape prior to control the degree of the deformations. Adaptive filters are used to model complex cardiac motion in the dynamic model of the system. Particle filters are utilized to recursively estimate the current state of the system over time. The proposed method is applied to recover biventricular deformations and validated with a numerical phantom and multiple real cardiac MRI datasets. The algorithm is evaluated with multiple experiments using fixed and varying image slice planes at each time step. For the real cardiac MRI datasets, the average root-mean-square tracking errors of 2.61 mm and 3.42 mm are reported respectively for the fixed and varying image slice planes. This work serves as a proof-of-concept study for modeling and tracking the cardiac surface deformations via a low-order probabilistic model with the future goal of utilizing this method for the targeted interventional cardiac procedures under MR image guidance. For the real cardiac MRI datasets, the presented method was able to track the points-of-interests located on different sections of the cardiac surface within a precision of 3 pixels. The analyses show that the use of deformable cardiac surface tracking algorithm can pave the way for performing precise targeted intracardiac ablation procedures under MRI guidance. The main contributions of this work are twofold. First, it presents a framework for the tracking of whole cardiac surface from a time sequence of single image slices. Second, it employs adaptive filters to incorporate motion information in the tracking of nonrigid cardiac surface motion for temporal coherence.
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影响因子：
10.6
作者：
Elen, An;Hermans, Jeroen;Suetens, Paul
通讯作者：
Suetens, Paul
影响因子：
19.5
作者：
KASS, M;WITKIN, A;TERZOPOULOS, D
通讯作者：
TERZOPOULOS, D
DOI：
10.1152/ajpheart.00442.2005
发表时间：
2005-11-01
影响因子：
4.8
作者：
Haber, I;Metaxas, DN;Axel, L
通讯作者：
Axel, L
在机器人辅助心脏跳动手术中，在心律失常的情况下主动跟踪跳动的心脏运动。
影响因子：
3.7
作者：
Tuna EE;Karimov JH;Liu T;Bebek Ö;Fukamachi K;Çavuşoğlu MC
通讯作者：
Çavuşoğlu MC
影响因子：
10.9
作者：
Bardinet, E;Cohen, L D;Ayache, N
通讯作者：
Ayache, N