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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Konukoglu, E., Sermesant, M., Clatz, O., Peyrat, J. M., Delingette, H., & Ayache, N. (2007). A recursive anisotropic fast marching approach to reaction diffusion equation: Application to tumor growth modeling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4584 LNCS, pp. 687–699). Springer Verlag. https://doi.org/10.1007/978-3-540-73273-0_57
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