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首页> 外文会议>International workshop on statistical atlases and computational models of the heart >From Image to Personalized Cardiac Simulation: Encoding Anatomical Structures into a Model-Based Segmentation Framework
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From Image to Personalized Cardiac Simulation: Encoding Anatomical Structures into a Model-Based Segmentation Framework

机译：从图像到个性化心脏模拟：将解剖结构编码到基于模型的分割框架中

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Whole organ scale patient specific biophysical simulations contribute to the understanding, diagnosis and treatment of complex diseases such as cardiac arrhythmia. However, many individual steps are required to bridge the gap from an anatomical scan to a personalized biophysical model. In biophysical modeling, differential equations are solved on spatial domains represented by volumetric meshes of high resolution and in model-based segmentation, surface or volume meshes represent the patient's geometry. We simplify the personalization process by representing the simulation mesh and additional relevant structures relative to the segmentation mesh. Using a surface correspondence preserving model-based segmentation algorithm, we facilitate the integration of anatomical information into biophysical models avoiding a complex processing pipeline. In a simulation study, we observe surface correspondence of up to 1.6 mm accuracy for the four heart chambers. We compare isotropic and anisotropic atrial excitation propagation in a personalized simulation.
机译：全器官规模的患者特定生物物理模拟有助于理解，诊断和治疗复杂的疾病，例如心律不齐。但是，需要许多单独的步骤来弥合从解剖扫描到个性化生物物理模型的差距。在生物物理建模中，微分方程在高分辨率的体积网格代表的空间域上求解，在基于模型的分割中，表面或体积网格代表患者的几何形状。通过表示仿真网格和相对于分段网格的其他相关结构，我们简化了个性化过程。使用基于表面对应关系保存模型的分割算法，我们有助于将解剖信息集成到生物物理模型中，从而避免了复杂的处理流程。在模拟研究中，我们观察到四个心脏腔室的表面对应度最高可达1.6 mm。我们在个性化仿真中比较了各向同性和各向异性的房颤激发传播。

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