Wong Ken C L, Zhang Heye, Liu Huafeng, Shi Pengcheng
B. Thomas Golisano College of Computing and Information Sciences, Rochester Institute of Technology, Rochester, New York, USA.
Acad Radiol. 2007 Nov;14(11):1341-9. doi: 10.1016/j.acra.2007.07.026.
To more reliably recover cardiac information from noise-corrupted, patient-specific measurements, it is essential to employ meaningful constraining models and adopt appropriate optimization criteria to couple the models with the measurements. Although biomechanical models have been extensively used for myocardial motion recovery with encouraging results, the passive nature of such constraints limits their ability to fully count for the deformation caused by active forces of the myocytes. To overcome such limitations, we propose to adopt a cardiac physiome model as the prior constraint for cardiac motion analysis.
The cardiac physiome model comprises an electric wave propagation model, an electromechanical coupling model, and a biomechanical model, which are connected through a cardiac system dynamics for a more complete description of the macroscopic cardiac physiology. Embedded within a multiframe state-space framework, the uncertainties of the model and the patient's measurements are systematically dealt with to arrive at optimal cardiac kinematic estimates and possibly beyond.
Experiments have been conducted to compare our proposed cardiac-physiome-model-based framework with the solely biomechanical model-based framework. The results show that our proposed framework recovers more accurate cardiac deformation from synthetic data and obtains more sensible estimates from real magnetic resonance image sequences.
With the active components introduced by the cardiac physiome model, cardiac deformations recovered from patient's medical images are more physiologically plausible.
为了更可靠地从受噪声干扰的患者特定测量中恢复心脏信息，采用有意义的约束模型并采用适当的优化标准将模型与测量相结合至关重要。尽管生物力学模型已被广泛用于心肌运动恢复并取得了令人鼓舞的结果，但此类约束的被动性质限制了它们充分考虑心肌主动力引起的变形的能力。为克服这些限制，我们建议采用心脏生理组模型作为心脏运动分析的先验约束。
心脏生理组模型包括一个电波传播模型、一个机电耦合模型和一个生物力学模型，它们通过心脏系统动力学连接，以更完整地描述宏观心脏生理学。嵌入多帧状态空间框架内，系统地处理模型和患者测量的不确定性，以获得最佳的心脏运动学估计，甚至可能更优。
已进行实验，将我们提出的基于心脏生理组模型的框架与仅基于生物力学模型的框架进行比较。结果表明，我们提出的框架能从合成数据中恢复更准确的心脏变形，并从真实磁共振图像序列中获得更合理的估计。
由于心脏生理组模型引入了主动成分，从患者医学图像中恢复的心脏变形在生理上更合理。
