Dhamala Jwala, Arevalo Hermenegild J, Sapp John, Horacek Milan, Wu Katherine C, Trayanova Natalia A, Wang Linwei
IEEE Trans Med Imaging. 2017 Sep;36(9):1966-1978. doi: 10.1109/TMI.2017.2697820. Epub 2017 Apr 25.
To obtain a patient-specific cardiac electro-physiological (EP) model, it is important to estimate the 3-D distributed tissue properties of the myocardium. Ideally, the tissue property should be estimated at the resolution of the cardiac mesh. However, such high-dimensional estimation faces major challenges in identifiability and computation. Most existing works reduce this dimension by partitioning the cardiac mesh into a pre-defined set of segments. The resulting low-resolution solutions have a limited ability to represent the underlying heterogeneous tissue properties of varying sizes, locations, and distributions. In this paper, we present a novel framework that, going beyond a uniform low-resolution approach, is able to obtain a higher resolution estimation of tissue properties represented by spatially non-uniform resolution. This is achieved by two central elements: 1) a multi-scale coarse-to-fine optimization that facilitates higher resolution optimization using the lower resolution solution and 2) a spatially adaptive decision criterion that retains lower resolution in homogeneous tissue regions and allows higher resolution in heterogeneous tissue regions. The presented framework is evaluated in estimating the local tissue excitability properties of a cardiac EP model on both synthetic and real data experiments. Its performance is compared with optimization using pre-defined segments. Results demonstrate the feasibility of the presented framework to estimate local parameters and to reveal heterogeneous tissue properties at a higher resolution without using a high number of unknowns.
为了获得特定患者的心脏电生理（EP）模型，估计心肌的三维分布式组织特性非常重要。理想情况下，组织特性应在心脏网格的分辨率下进行估计。然而，这种高维估计在可识别性和计算方面面临重大挑战。大多数现有工作通过将心脏网格划分为一组预定义的段来降低维度。由此产生的低分辨率解决方案在表示不同大小、位置和分布的潜在异质组织特性方面能力有限。在本文中，我们提出了一个新颖的框架，该框架超越了统一的低分辨率方法，能够获得由空间非均匀分辨率表示的组织特性的更高分辨率估计。这通过两个核心要素实现：1）多尺度粗到细优化，利用低分辨率解决方案促进更高分辨率的优化；2）空间自适应决策标准，在均匀组织区域保留低分辨率，在异质组织区域允许更高分辨率。在合成数据和真实数据实验中，对所提出的框架在估计心脏EP模型的局部组织兴奋性特性方面进行了评估。将其性能与使用预定义段的优化进行了比较。结果证明了所提出框架在不使用大量未知数的情况下以更高分辨率估计局部参数和揭示异质组织特性的可行性。
