Konukoglu Ender, Sermesant Maxime, Clatz Olivier, Peyrat Jean-Marc, Delingette Hervé, Ayache Nicholas
Asclepios Research Project, INRIA Sophia Antipolis, France.
Inf Process Med Imaging. 2007;20:687-99. doi: 10.1007/978-3-540-73273-0_57.
Bridging the gap between clinical applications and mathematical models is one of the new challenges of medical image analysis. In this paper, we propose an efficient and accurate algorithm to solve anisotropic Eikonal equations, in order to link biological models using reaction-diffusion equations to clinical observations, such as medical images. The example application we use to demonstrate our methodology is tumor growth modeling. We simulate the motion of the tumor front visible in images and give preliminary results by solving the derived anisotropic Eikonal equation with the recursive fast marching algorithm.
弥合临床应用与数学模型之间的差距是医学图像分析的新挑战之一。在本文中，我们提出了一种高效且准确的算法来求解各向异性的程函方程，以便将使用反应扩散方程的生物学模型与临床观察结果（如医学图像）联系起来。我们用于演示我们方法的示例应用是肿瘤生长建模。我们模拟图像中可见的肿瘤前沿的运动，并通过使用递归快速行进算法求解导出的各向异性程函方程给出初步结果。
