Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA.
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA.
Sci Rep. 2023 Jan 25;13(1):1387. doi: 10.1038/s41598-023-28578-0.
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.
这项研究提出了一种基于粒子滤波器的框架，用于从单张磁共振成像 (MRI) 切片的时间序列中跟踪心脏表面，其未来目标是利用该框架为心血管磁共振介入程序提供支持，这些程序依赖于从 MRI 数据中准确、在线地跟踪心脏表面。该框架利用心脏表面的低阶参数变形模型。一个随机动态系统表示心脏表面运动。变形模型用于引入形状先验来控制变形程度。自适应滤波器用于在系统的动态模型中对复杂的心脏运动进行建模。粒子滤波器用于随时间递归地估计系统的当前状态。该方法应用于恢复双心室变形，并通过数值体模和多个真实心脏 MRI 数据集进行验证。该算法使用多个实验进行评估，在每个时间步使用固定和变化的图像切片平面。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，分别报告了平均均方根跟踪误差为 2.61mm 和 3.42mm。这项工作是通过低阶概率模型对心脏表面变形进行建模和跟踪的概念验证研究，未来目标是在 MR 图像引导下将该方法用于靶向心脏介入程序。对于真实的心脏 MRI 数据集，所提出的方法能够以 3 个像素的精度跟踪位于心脏表面不同部位的兴趣点。分析表明，使用可变形心脏表面跟踪算法可以为在 MRI 引导下进行精确的靶向心内消融程序铺平道路。这项工作的主要贡献有两个方面。首先，它提出了一种从单张图像切片的时间序列中跟踪整个心脏表面的框架。其次，它采用自适应滤波器在跟踪非刚性心脏表面运动时纳入运动信息，以实现时间连贯性。
