MODELING INTERNAL RADIATION THERAPY

Egon L. van den Broek, Theo E. Schouten

Abstract

A new technique is introduced to model (internal) radiation therapy. It is founded on morphological processing, in particular distance transforms. Its formal basis is presented as well as its implementation via the fast exact Euclidean distance (FEED) transform. Its use for all variations of internal radiation therapy is described. In a benchmark FEED proved to be truly exact as well as faster than a comparable technique. In particular, this 100% accuracy can be of crucial importance for radiation therapy purposes as the balance between maximization of treatment effect and doses that cause unwanted damage to health tissue is fragile. Through the modeling technique presented here this balance can be secured.

References

  1. Borgefors, G., Nyström, I., and Sanniti di Baja, G. (2003). Discrete geometry for computer imagery. Discrete Applied Mathematics, 125(1):[special issue].
  2. Bortfeld, T. (2006). IMRT: A review and preview. Physics in Medicine and Biology, 51(13):R363-R379.
  3. Brianzoni, E., Rossi, G., and Proietti, A. (2008). Gross Tumor Volume and Clinical Target Volume: Anatomical Computed Tomography and Functional FDG-PET, volume 2, chapter 13, pages 225-234. Burlington, MA, USA: Elsevier Academic Press.
  4. Censor, Y., Galvin, J. M., Xiao, Y., and Langer, M. (2008). Linear and nonlinear models and algorithms in intensity-modulated radiation therapy (IMRT). Linear Algebra and its Applications, 428(5-6):1203- 1206.
  5. S¸. Ig?dem, Alc¸o, G., Ercan, T., Ü nalan, B., Kara, B., Geceer, G., Akman, C., Zengin, F. O., Atilla, S., and Okkan, S. (2010). The application of Positron Emission Tomography/Computed Tomography in radiation treatment planning: Effect on gross target volume definition and treatment management. Clinical Oncology, 22(3):173-178.
  6. Cuisenaire, O. and Macq, B. (1999). Fast Euclidean transformation by propagation using multiple neighborhoods. Computer Vision and Image Understanding, 76(2):163-172.
  7. Fabbri, R., da F. Costa, L., Torelli, J. C., and Bruno, O. M. (2008). 2D Euclidean distance transform algorithms: A comparative survey. ACM Computing Surveys, 40(1):Article 2.
  8. Lu, Y., Jiang, T., and Zang, Y. (2003). Region growing method for the analysis of functional MRI data. NeuroImage, 20(1):455-465.
  9. Mazonakis, M., Damilakis, J., Varveris, H., Prassopoulos, P., and Gourtsoyiannis, N. (2001). Image segmentation in treatment planning for prostate cancer using the region growing technique. British Journal of Radiology, 74(879):243-249.
  10. Razmjooei, S. and Dudek, P. (2010). Approximating Euclidean distance transform with simple operations in cellular processor arrays. In IEEE Proceedings of the 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA), pages 181- 185, Berkely, CA, USA. IEEE.
  11. Rosenfeld, A. and Pfaltz, J. L. (1966). Sequential operations in digital picture processing. Journal of the ACM, 13(4):471-494.
  12. Salvi, J., Matabosch, C., Fofi, D., and Forest, J. (2007). A review of recent range image registration methods with accuracy evaluation. Image and Vision Computing, 25(5):578-596.
  13. Schouten, T. E., Kuppens, H. C., and van den Broek, E. L. (2006). Three dimensional fast exact Euclidean distance (3D-FEED) maps. Proceedings of SPIE (Vision Geometry XIV), 6066:60660F.
  14. Schouten, T. E. and van den Broek, E. L. (2004). Fast Exact Euclidean Distance (FEED) Transformation. In Kittler, J., Petrou, M., and Nixon, M., editors, Proceedings of the 17th IEEE International Conference on Pattern Recognition (ICPR 2004), volume 3, pages 594-597, Cambridge, United Kingdom.
  15. Schouten, T. E. and van den Broek, E. L. (2010). Incremental Distance Transform (IDT). In Erc¸il, A., C¸ etin, M., Boyer, K., and Lee, S.-W., editors, Proceedings of the 20th IEEE International Conference on Pattern Recognition (ICPR), pages 237-240, Istanbul, Turkey.
  16. Shih, F. Y. and Wu, Y.-T. (2004). Fast Euclidean distance transformation in two scans using a 3 × 3 neighborhood. Computer Vision and Image Understanding, 93(2):195-205.
  17. Zaidi, H., Vees, H., and Wissmeyer, M. (2009). Molecular PET/CT imaging-guided radiation therapy treatment planning. Academic Radiology, 16(9):1108-1133.
Download


Paper Citation


in Harvard Style

L. van den Broek E. and E. Schouten T. (2011). MODELING INTERNAL RADIATION THERAPY . In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2011) ISBN 978-989-8425-36-2, pages 228-233. DOI: 10.5220/0003172202280233


in Bibtex Style

@conference{bioinformatics11,
author={Egon L. van den Broek and Theo E. Schouten},
title={MODELING INTERNAL RADIATION THERAPY},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2011)},
year={2011},
pages={228-233},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003172202280233},
isbn={978-989-8425-36-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2011)
TI - MODELING INTERNAL RADIATION THERAPY
SN - 978-989-8425-36-2
AU - L. van den Broek E.
AU - E. Schouten T.
PY - 2011
SP - 228
EP - 233
DO - 10.5220/0003172202280233