Grandits Thomas, Pezzuto Simone, Lubrecht Jolijn M, Pock Thomas, Plank Gernot, Krause Rolf
Institute of Computer Graphics and Vision Graz University of Technology.
BioTechMed-Graz, Austria.
Stat Atlases Comput Models Heart. 2021;12592:76-86. doi: 10.1007/978-3-030-68107-4_8. Epub 2021 Jan 29.
Electroanatomical mapping, a keystone diagnostic tool in cardiac electrophysiology studies, can provide high-density maps of the local electric properties of the tissue. It is therefore tempting to use such data to better individualize current patient-specific models of the heart through a data assimilation procedure and to extract potentially insightful information such as conduction properties. Parameter identification for state-of-the-art cardiac models is however a challenging task. In this work, we introduce a novel inverse problem for inferring the anisotropic structure of the conductivity tensor, that is fiber orientation and conduction velocity along and across fibers, of an eikonal model for cardiac activation. The proposed method, named PIEMAP, performed robustly with synthetic data and showed promising results with clinical data. These results suggest that PIEMAP could be a useful supplement in future clinical workflows of personalized therapies.
电解剖标测是心脏电生理学研究中的关键诊断工具，可提供组织局部电学特性的高密度图谱。因此，人们很想通过数据同化程序利用这些数据，使当前针对患者个体的心脏模型更加个性化，并提取诸如传导特性等潜在的有价值信息。然而，对于先进的心脏模型进行参数识别是一项具有挑战性的任务。在这项工作中，我们提出了一个新的反问题，用于推断心脏激活的程函模型中电导率张量的各向异性结构，即纤维方向以及沿纤维和跨纤维的传导速度。所提出的方法名为PIEMAP，在合成数据上表现稳健，在临床数据上也显示出有前景的结果。这些结果表明，PIEMAP可能是未来个性化治疗临床工作流程中的一个有用补充。
