3D Visualisation of Tumour-Induced Angiogenesis Using the CUDA Programming Model and OpenGL Interoperability

Paul M. Darbyshire

doi:doi:10.11648/j.jctr.20150305.11

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3D Visualisation of Tumour-Induced Angiogenesis Using the CUDA Programming Model and OpenGL Interoperability

Paul M. Darbyshire

Published in Journal of Cancer Treatment and Research (Volume 3, Issue 5)

Received: 29 September 2015 Accepted: 24 October 2015 Published: 10 November 2015

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Abstract

In this paper we solve a complex discrete-continuous model of tumour-induced angiogenesis using an explicit time-stepping FDM and simultaneously simulate the model dynamics in 3D. The interoperability between the CUDA programming model and the graphics hardware through OpenGL allows us to generate dynamic interactive 3D realistic visualisations. We use CUDA for the complex parallel calculations and deploy OpenGL for on-the-fly 3D visualisation of the numerical simulations. Clearly, being able to link the numerical results of complex mathematical models to interactive 3D visualisations that can literally update instantaneously to varying model parameters, should provide an invaluable tool for clinical physicians and research scientists. We also give an overview of current medical imaging techniques for studying microcirculatory and blood flow dynamics at the cellular level and indicate how the results presented here could offer potential for future developments in this area.

Published in	Journal of Cancer Treatment and Research (Volume 3, Issue 5)
DOI	10.11648/j.jctr.20150305.11
Page(s)	53-65
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

3D Cancer Modelling, 3D Visualisation, Medical Imaging, High Performance Computing, Compute Unified Device Architecture (CUDA), Graphical Processing Unit (GPU), Open Graphics Library (OpenGL)

References

[1]	Molnár, Jr. F., Izsák, F., Mészáros, R., and Lagzi, I. Simulation of reaction–diffusion processes in three dimensions using CUDA. Chemometrics and Intelligent Laboratory Systems. 108, 76–85. 2011.
[2]	Kirtzic, J. S., Allen, D. and Daescu, O. Applying the Parallel GPU Model to Radiation Therapy Treatment. International Conference on Parallel and Distributed Processing Techniques and Applications. 2013.
[3]	Nvidia Corporation. How GPU-Driven Drug Discovery is Finding New Targets to Cure Cancer. 2015.
[4]	Nvidia Corporation. Compute the Cure: How GPU-Driven Cancer Therapies Overtook One Man’s Astronaut Dreams. 2015.
[5]	Nvidia Corporation. Zapping Cost of Cancer Treatment Using Laser-Driven Ion Accelerators and GPU Computing. 2015.
[6]	Worecki, M., and Wcislo, R. GPU Enhanced Simulation of Angiogenesis Computer Science. 13 (1). 2012.
[7]	Borys, D., Psiuk-Maksymowicz, K., and Swierniak, A. Parallel Implementations of Numerical Simulation of the Vascular Solid Tumour Growth Model under the Action of Therapeutic Agents. Biotechno: Sixth International Conference on Bioinformatics, Biocomputational Systems and Biotechnologies. 2014.
[8]	Darbyshire,P. M. Coupled Nonlinear Partial Differential Equations Describing Avascular Tumour Growth Are Solved Numerically Using Parallel Programming to Assess Computational Speedup. Computational Biology and Bioinformatics. Vol. 3, No. 5, 65-73. 2015.
[9]	Darbyshire,P. M. The Numerical Solution of a Hybrid Continuous-Discrete Model of Tumour-Induced Angiogenesis is Implemented in Parallel and Performance Improvements Analysed. European Journal of Biophysics. Vol. 7, No. 4, 167-182. 2015.
[10]	Darbyshire,P. M. Performance Optimisations for a Numerical Solution to a 3D Model of Tumour-Induced Angiogenesis on a Parallel Programming Platform. Cell Biology. Vol. 3, No. 3, 38-49. 2015.
[11]	Staton, C. A., Reed. M. W. R. and Brown, N. J. A critical analysis of current in vitro and in vivo angiogenesis assays. International Journal of Experimental Pathology, 90, 195–221. 2009.
[12]	Hanahan, D and Folkman, J. Patterns and emerging mechanisms of the angiogenic switch during tumorigenesis. Cell, 86, 353–364. 1996.
[13]	Albini, A., Tosetti, A. F., Li, W. V., Noonan, D. M. and Li, W. W. Cancer prevention by targeting angiogenesis Nature Reviews Clinical Oncology 9, 498-509. 2012.
[14]	Ferrara, N. and Kerbel, R. S. Angiogenesis as a therapeutic target. Nature, 438 967–974. 2005.
[15]	Carmeliet, P. Angiogenesis in life, disease and medicine. Nature, 438: 932–936. 2005.
[16]	Bouard S. de, Herlin, P. and Christensen, J. G. Antiangio-genic and anti-invasive effects of sunitinib on experimental human glioblastoma. Neuro-Oncology, Vol. 9, No. 4, 412– 423. 2007.
[17]	Norden, A. D, Drappatz, J. and Wen P. Y. Novel antiangiogenic therapies for malignant gliomas. The Lancet Neurology, Vol. 7, No. 12, 1152–1160. 2008.
[18]	Peirce, S. M. Computational and mathematical modeling of angiogenesis. Microcirculation, 15(8), 739–751. 2008.
[19]	M. Scianna, M., Bell. C. and Preziosi L. A review of mathematical models for the formation of vascular networks. Oxford Centre for Collaborative Applied Mathematics. 2012.
[20]	Stephanou, A., McDougall, S. R., Anderson, A.R.A. and Chaplain, M. A. J. Mathematical Modelling of Flow in 2D and 3D Vascular Networks: Applications to Anti-Angiogenic and Chemotherapeutic Drug Strategies. Mathematical and Computer Modelling, 41, 1137-1156. 2005.
[21]	Shirinifard, A. J., Scott Gens, J., Zaitlen, B. L., Popławski, N. J., Swat, M., and Glazier, J. A. 3D Multi-Cell Simulation of Tumor Growth and Angiogenesis. PLoS One, 4(10). 2009.
[22]	Perfahl, H., Byrne, H. M., Chen, T., Estrella, V., Alarcon, T., Lapin, A., Gatenby, R. J., Gillies, M.C., P.K., Maini, Reuss, M., and Owen, M. R. 3D Multiscale Modelling of Angiogenesis and Vascular Tumour Growth. Chapter in Micro and Nano Flow Systems for Bioanalysis. Vol. 2, 29-48. 2012.
[23]	Rejniak A. K. and Anderson A.R.A. Hybrid models of tumor growth. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 3(1), 115–125. 2011.
[24]	Nvidia Corporation. CUDA C programming guide. Version 6.0. 2014.
[25]	Paweletz, N. and M. Knierim M. Tumor-related angiogenesis. Critical Reviews in Oncology and Hematology, 9, 197–242. 1989.
[26]	Paku, S. and N. Paweletz. First steps of tumor-related angiogenesis. Laboratory Investigation, 65, 334–346. 1991.
[27]	Schor S. L., Schor A. M., Brazill G. W. The effects of fibronectin on the migration of human foreskin fibroblasts and Syrian hamster melanoma cells into three-dimensional gels of lattice collagen fibres. Journal of Cell Science, 48, 301–314, 1981.
[28]	Bowersox J. C. and Sorgente N. Chemotaxis of aortic endothelial cells in response to fibronectin. Cancer Research 42, 2547–2551. 1982.
[29]	Quigley J. P., Lacovara J., and Cramer E. B. The directed migration of B-16 melanoma-cells in response to a haptotactic chemotactic gradient of fibronectin. Journal of Cell Biology 97, A450–451. 1983.
[30]	Stokes C. L., Lauffenburger D. A., and Williams S. K. Migration of individual microvessel endothelial cells: stochastic model and parameter measurement. Journal of Cell Science, 99: 419–430. 1991.
[31]	Stokes C. L., Rupnick M. A., Williams S. K., and Lauffenburger D. A. Chemotaxis of human microvessel endothelial cells in response to acidic fibroblast growth factor. Laboratory Investigation, 63, 657–668, 1991.
[32]	Stokes C. L., and Lauffenburger D. A. Analysis of the roles of microvessel endothelial cell random motility and chemotaxis in angiogenesis. Journal of Theoretical Biology, 152, 377–403. 1991.
[33]	Anderson, A.R.A. and Chaplain, M. Continuous and discrete mathematical models of tumour-induced angiogenesis, Bulletin of Mathematical Biology, 60, 857-900. 1998.
[34]	Anderson, A., Sleeman, B. D. S., Young I. M., and Griffiths, B. S. Nematode movement along a chemical gradient in a structurally heterogeneous environment: II. Theory. Fundamental and Applied Nematology, 20, 165–172. 1997.
[35]	Muthukkaruppan, V. R., Kubai, L., Auerbach, R. Tumorinduced neovascularization in the mouse eye. Journal of the National Cancer Institute, 69, 699–705. 1982.
[36]	Williams, S. K. Isolation and culture of microvessel and large-vessel endothelial cells; their use in transport and clinical studies. Microvascular Perfusion and Transport in Health and Disease, 204–245. 1987.
[37]	Cheng, J., Grossman, M and McKercher, Ty. Professional CUDA C Programming. Wrox. 2014.
[38]	Beerling, E., Ritsma, L., Vrisekoop N., Derksen P. W. B. and Rheenen J. van. Intravital microscopy: new insights into metastasis of Tumors. Journal of Cell Science, 124, 299-310. 2011.
[39]	Vakoc, B. J., Lanning, R. M., Tyrrell, J. A., Padera, T. P., Bartlett, L. A., Stylianopoulos, T., Munn, L. L., Tearney, G. J., Fukumura, D., Jain, R. K. et al. Three dimensional microscopy of the tumor microenvironment in vivo using optical frequency domain imaging. Nature Medicine 15, 1219-1223. 2009.
[40]	Abdul-Karim, M.-A., Al-Kofahi, K., Brown, E. B., Jain, R. K. and Roysam, B. Automated tracing and change analysis of angiogenic vasculature from in vivo multiphoton confocal image time series. Journal of Microvascular. Research. 66, 113-125. 2003.
[41]	Hoffman, R. Imaging cancer dynamics in vivo at the tumor and cellular level with fluorescent proteins. Clinical and Experimental Metastasis 26, 345-355. 2009.
[42]	Le Dévédec, S., Lalai, R., Pont, C., de Bont, H. and van de Water, B. Two photon intravital multicolor imaging combined with inducible gene expression to distinguish metastatic behavior of breast cancer cells in vivo. Molecular Imaging Biology, 13(1):67-77. 2011.

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APA Style

Paul M. Darbyshire. (2015). 3D Visualisation of Tumour-Induced Angiogenesis Using the CUDA Programming Model and OpenGL Interoperability. Journal of Cancer Treatment and Research, 3(5), 53-65. https://doi.org/10.11648/j.jctr.20150305.11

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ACS Style

Paul M. Darbyshire. 3D Visualisation of Tumour-Induced Angiogenesis Using the CUDA Programming Model and OpenGL Interoperability. J. Cancer Treat. Res. 2015, 3(5), 53-65. doi: 10.11648/j.jctr.20150305.11

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AMA Style

Paul M. Darbyshire. 3D Visualisation of Tumour-Induced Angiogenesis Using the CUDA Programming Model and OpenGL Interoperability. J Cancer Treat Res. 2015;3(5):53-65. doi: 10.11648/j.jctr.20150305.11

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@article{10.11648/j.jctr.20150305.11,
  author = {Paul M. Darbyshire},
  title = {3D Visualisation of Tumour-Induced Angiogenesis Using the CUDA Programming Model and OpenGL Interoperability},
  journal = {Journal of Cancer Treatment and Research},
  volume = {3},
  number = {5},
  pages = {53-65},
  doi = {10.11648/j.jctr.20150305.11},
  url = {https://doi.org/10.11648/j.jctr.20150305.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jctr.20150305.11},
  abstract = {In this paper we solve a complex discrete-continuous model of tumour-induced angiogenesis using an explicit time-stepping FDM and simultaneously simulate the model dynamics in 3D. The interoperability between the CUDA programming model and the graphics hardware through OpenGL allows us to generate dynamic interactive 3D realistic visualisations. We use CUDA for the complex parallel calculations and deploy OpenGL for on-the-fly 3D visualisation of the numerical simulations. Clearly, being able to link the numerical results of complex mathematical models to interactive 3D visualisations that can literally update instantaneously to varying model parameters, should provide an invaluable tool for clinical physicians and research scientists. We also give an overview of current medical imaging techniques for studying microcirculatory and blood flow dynamics at the cellular level and indicate how the results presented here could offer potential for future developments in this area.},
 year = {2015}
}

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TY  - JOUR
T1  - 3D Visualisation of Tumour-Induced Angiogenesis Using the CUDA Programming Model and OpenGL Interoperability
AU  - Paul M. Darbyshire
Y1  - 2015/11/10
PY  - 2015
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AB  - In this paper we solve a complex discrete-continuous model of tumour-induced angiogenesis using an explicit time-stepping FDM and simultaneously simulate the model dynamics in 3D. The interoperability between the CUDA programming model and the graphics hardware through OpenGL allows us to generate dynamic interactive 3D realistic visualisations. We use CUDA for the complex parallel calculations and deploy OpenGL for on-the-fly 3D visualisation of the numerical simulations. Clearly, being able to link the numerical results of complex mathematical models to interactive 3D visualisations that can literally update instantaneously to varying model parameters, should provide an invaluable tool for clinical physicians and research scientists. We also give an overview of current medical imaging techniques for studying microcirculatory and blood flow dynamics at the cellular level and indicate how the results presented here could offer potential for future developments in this area.
VL  - 3
IS  - 5
ER  -

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Author Information

Paul M. Darbyshire

Computational Biophysics Group, Algenet Cancer Research, Nottingham, UK

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APA Style

Paul M. Darbyshire. (2015). 3D Visualisation of Tumour-Induced Angiogenesis Using the CUDA Programming Model and OpenGL Interoperability. Journal of Cancer Treatment and Research, 3(5), 53-65. https://doi.org/10.11648/j.jctr.20150305.11

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ACS Style

Paul M. Darbyshire. 3D Visualisation of Tumour-Induced Angiogenesis Using the CUDA Programming Model and OpenGL Interoperability. J. Cancer Treat. Res. 2015, 3(5), 53-65. doi: 10.11648/j.jctr.20150305.11

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AMA Style

Paul M. Darbyshire. 3D Visualisation of Tumour-Induced Angiogenesis Using the CUDA Programming Model and OpenGL Interoperability. J Cancer Treat Res. 2015;3(5):53-65. doi: 10.11648/j.jctr.20150305.11

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@article{10.11648/j.jctr.20150305.11,
  author = {Paul M. Darbyshire},
  title = {3D Visualisation of Tumour-Induced Angiogenesis Using the CUDA Programming Model and OpenGL Interoperability},
  journal = {Journal of Cancer Treatment and Research},
  volume = {3},
  number = {5},
  pages = {53-65},
  doi = {10.11648/j.jctr.20150305.11},
  url = {https://doi.org/10.11648/j.jctr.20150305.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jctr.20150305.11},
  abstract = {In this paper we solve a complex discrete-continuous model of tumour-induced angiogenesis using an explicit time-stepping FDM and simultaneously simulate the model dynamics in 3D. The interoperability between the CUDA programming model and the graphics hardware through OpenGL allows us to generate dynamic interactive 3D realistic visualisations. We use CUDA for the complex parallel calculations and deploy OpenGL for on-the-fly 3D visualisation of the numerical simulations. Clearly, being able to link the numerical results of complex mathematical models to interactive 3D visualisations that can literally update instantaneously to varying model parameters, should provide an invaluable tool for clinical physicians and research scientists. We also give an overview of current medical imaging techniques for studying microcirculatory and blood flow dynamics at the cellular level and indicate how the results presented here could offer potential for future developments in this area.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - 3D Visualisation of Tumour-Induced Angiogenesis Using the CUDA Programming Model and OpenGL Interoperability
AU  - Paul M. Darbyshire
Y1  - 2015/11/10
PY  - 2015
N1  - https://doi.org/10.11648/j.jctr.20150305.11
DO  - 10.11648/j.jctr.20150305.11
T2  - Journal of Cancer Treatment and Research
JF  - Journal of Cancer Treatment and Research
JO  - Journal of Cancer Treatment and Research
SP  - 53
EP  - 65
PB  - Science Publishing Group
SN  - 2376-7790
UR  - https://doi.org/10.11648/j.jctr.20150305.11
AB  - In this paper we solve a complex discrete-continuous model of tumour-induced angiogenesis using an explicit time-stepping FDM and simultaneously simulate the model dynamics in 3D. The interoperability between the CUDA programming model and the graphics hardware through OpenGL allows us to generate dynamic interactive 3D realistic visualisations. We use CUDA for the complex parallel calculations and deploy OpenGL for on-the-fly 3D visualisation of the numerical simulations. Clearly, being able to link the numerical results of complex mathematical models to interactive 3D visualisations that can literally update instantaneously to varying model parameters, should provide an invaluable tool for clinical physicians and research scientists. We also give an overview of current medical imaging techniques for studying microcirculatory and blood flow dynamics at the cellular level and indicate how the results presented here could offer potential for future developments in this area.
VL  - 3
IS  - 5
ER  -

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