Intelligent Sensor-Actuator-Systems Laboratory, Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany.
Int J Comput Assist Radiol Surg. 2011 May;6(3):387-99. doi: 10.1007/s11548-010-0517-5. Epub 2010 Aug 6.
Tracking of beating heart motion in a robotic surgery system is required for complex cardiovascular interventions.
A heart surface motion tracking method is developed, including a stochastic physics-based heart surface model and an efficient reconstruction algorithm. The algorithm uses the constraints provided by the model that exploits the physical characteristics of the heart. The main advantage of the model is that it is more realistic than most standard heart models. Additionally, no explicit matching between the measurements and the model is required. The application of meshless methods significantly reduces the complexity of physics-based tracking.
Based on the stochastic physical model of the heart surface, this approach considers the motion of the intervention area and is robust to occlusions and reflections. The tracking algorithm is evaluated in simulations and experiments on an artificial heart. Providing higher accuracy than the standard model-based methods, it successfully copes with occlusions and provides high performance even when all measurements are not available.
Combining the physical and stochastic description of the heart surface motion ensures physically correct and accurate prediction. Automatic initialization of the physics-based cardiac motion tracking enables system evaluation in a clinical environment.
在机器人手术系统中需要跟踪心脏的跳动运动，以便进行复杂的心血管介入治疗。
开发了一种心脏表面运动跟踪方法，包括基于随机物理的心脏表面模型和高效的重建算法。该算法利用模型提供的约束条件，利用心脏的物理特性。该模型的主要优点是比大多数标准心脏模型更真实。此外，不需要对测量值和模型进行显式匹配。基于无网格方法的应用显著降低了基于物理的跟踪的复杂性。
基于心脏表面的随机物理模型，该方法考虑了介入区域的运动，对遮挡和反射具有鲁棒性。该跟踪算法在人工心脏的模拟和实验中进行了评估。与基于标准模型的方法相比，它提供了更高的准确性，成功地应对了遮挡，并在并非所有测量值都可用的情况下提供了高性能。
将心脏表面运动的物理和随机描述相结合，可确保准确的预测。基于物理的心脏运动跟踪的自动初始化使得能够在临床环境中评估系统。
