A Graphical Tool and Methods for Assessing Margin Definition From Daily Image Deformations

  • Aditya P. Apte Department of Radiation Oncology, School of Medicine, Washington University in Saint Louis, MO, 63110
  • Rawan Al-Lozi Department of Radiation Oncology, School of Medicine, Washington University in Saint Louis, MO, 63110
  • Gisele Pereira Department of Radiation Oncology, School of Medicine, Washington University in Saint Louis, MO, 63110
  • Matthew Johnson Department of Radiation Oncology, School of Medicine, Washington University in Saint Louis, MO, 63110
  • David Mansur Department of Radiation Oncology, School of Medicine, Washington University in Saint Louis, MO, 63110
  • Issam M. El Naqa Department of Radiation Oncology, School of Medicine, Washington University in Saint Louis, MO, 63110

Abstract

Estimating the proper margins for the planning target volume (PTV) could be a challenging task in cases where the organ undergoes significant changes during the course of radiotherapy treatment. Developments in image-guidance and the presence of onboard imaging technologies facilitate the process of correcting setup errors. However, estimation of errors to organ motions remain an open question due to the lack of proper software tools to accompany these imaging technological advances. Therefore, we have developed a new tool for visualization and quantification of deformations from daily images. The tool allows for estimation of tumor coverage and normal tissue exposure as a function of selected margin (isotropic or anisotropic). Moreover, the software allows estimation of the optimal margin based on the probability of an organ being present at a particular location. Methods based on swarm intelligence, specifically Ant Colony Optimization (ACO) are used to provide an efficient estimate of the optimal margin extent in each direction. ACO can provide global optimal solutions in highly nonlinear problems such as margin estimation. The proposed method is demonstrated using cases from gastric lymphoma with daily TomoTherapy megavoltage CT (MVCT) contours. Preliminary results using Dice similarity index are promising and it is expected that the proposed tool will be very helpful and have significant impact for guiding future margin definition protocols.
Published
27-09-2017
How to Cite
Apte, A., Al-Lozi, R., Pereira, G., Johnson, M., Mansur, D., & El Naqa, I. (2017). A Graphical Tool and Methods for Assessing Margin Definition From Daily Image Deformations. Journal of Radiation Oncology Informatics, 2(1), 9-19. https://doi.org/10.5166/jroi-2-1-7
Section
Articles