论文标题
高斯工艺歧管插值，用于概率性房屋激活图和不确定传导速度
Gaussian Process Manifold Interpolation for Probabilistic Atrial Activation Maps and Uncertain Conduction Velocity
论文作者
论文摘要
在心房颤动的患者中，局部激活时间（LAT）图通常用于表征患者病理生理学。 LAT图的梯度可用于计算直接与材料电导率有关的传导速度（CV），并可以提供对房屋底物特性的重要度量。在简历计算中包括不确定性将有助于解释这些测量值的可靠性。在这里，我们基于对减少的高斯工艺（GP）的最新见解，以直接在人类心脏的歧管上对不确定的LAT进行概率插值。我们的高斯过程歧管插值（GPMI）方法解释了心房拓扑，并允许计算预测CV的统计数据。我们在两个临床病例上演示了我们的方法，并对模拟的地面真相进行验证。 CV不确定性取决于数据密度，波传播方向和CV幅度。 GPMI适用于非欧国歧管上其他不确定数量的概率插值。
In patients with atrial fibrillation, local activation time (LAT) maps are routinely used for characterising patient pathophysiology. The gradient of LAT maps can be used to calculate conduction velocity (CV), which directly relates to material conductivity and may provide an important measure of atrial substrate properties. Including uncertainty in CV calculations would help with interpreting the reliability of these measurements. Here, we build upon a recent insight into reduced-rank Gaussian processes (GP) to perform probabilistic interpolation of uncertain LAT directly on human atrial manifolds. Our Gaussian Process Manifold Interpolation (GPMI) method accounts for the topology of the atria, and allows for calculation of statistics for predicted CV. We demonstrate our method on two clinical cases, and perform validation against a simulated ground truth. CV uncertainty depends on data density, wave propagation direction, and CV magnitude. GPMI is suitable for probabilistic interpolation of other uncertain quantities on non-Euclidean manifolds.
