Graph-based Framework for 3-D Vascular Dynamics Simulation

Łazarz, Radosław

doi:10.1016/j.procs.2016.11.048

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…We assume that the vessel network is made of short segments with length of the mesh size. Due to a very specific way the blood network in the skin tissue is arranged (see Figure 1), comparing to a homogeneous tissue (see Łazarz, 2016), the algorithm for blood network creation adds the various layers of vessels in subsequent iterations. All of them are made of a series of line segments of the length equal to a “ segment_length ” parameter.…”

Section: Tumor Modelmentioning

confidence: 99%

“…Unlike melanoma setup, the topology of a blood vascular network for the homogeneous cancer environment resembles that from Łazarz (2016). Instead of many layers, only one “ layer ” is defined.…”

Section: Tumor Modelmentioning

confidence: 99%

“…It is well known (see e.g. Łazarz, 2016; Rieger et al, 2016) that the network of blood vessels inside and close to the tumor tissue is dynamically unstable. The interactions between the processes of new vessels creation due to angiogenesis and the shearing forces coming from purely mechanical stress stimulated by increasing tumor mass result in unsystematic blood flow in the vasculature.…”

Section: Tumor Modelmentioning

confidence: 99%

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Efficient model of tumor dynamics simulated in multi-GPU environment

Kłusek

Łoś

Paszyński

et al. 2018

The International Journal of High Performance Computing Applica

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The application of computer simulation as a tool in predicting cancer dynamics (e.g. during anticancer therapy) requires tumor models, which are nontrivial and, simultaneously, not computationally demanding. To this end, both the level of details and computational efficiency of the model should be well balanced. The restrictions on computational time are forced by very demanding data assimilation process in the phase of parameters learning and their correction on the basis of incoming medical data. Herein we present a very efficient multi-GPU/CUDA implementation of three-dimensional (3-D) cancer model which allows for simulating both the growth and treatment phases of tumor dynamics. We demonstrate that the interaction between the tissue and the discrete network of blood vessels is a crucial component, which influences considerably the simulation time. Here we present a new solution which eliminates this flaw. We show also that the efficiency of our model does not depend on the complexity of tumor setup. As an example, we confront the growth of tumor in a simple and homogeneous environment with melanoma evolution, which proliferates in a complex environment of human skin. Consequently, the 3-D simulation of a tumor growth up to 1 cm in diameter requires an hour of computations on a midrange multi-GPU server.

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Section: Tumor Modelmentioning

confidence: 99%

Section: Tumor Modelmentioning

confidence: 99%