SU‐E‐T‐25: A Learning Method for Assessing Margin Definition from Daily Image Deformations

Purpose: To investigate a new tool for quantification of deformations and optimal margin estimation from daily images using learning algorithms based on swarm intelligence. Methods: Swarm intelligence methods, 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 stomach lymphoma with daily TomoTherapy megavoltage CT (MVCT) contours. Results: The process consists of two main steps that involve deriving organ motion probability and estimating the optimal margin. For motion probability, we used methods based on rigid registration. The estimation of the optimal margin was carried out using swarm intelligence based on the ACO algorithm. Preliminary results using Dice similarity index are promising. In order to achieve 95% coverage, the ACO ran for 25 nest relocations and converged in about 10 iterations with an optimal Dice metric of 0.88. It is expected that the proposed method will be helpful for guiding future margin estimation protocols. Conclusions: In this work, we have presented a new software tool and an algorithm for estimating organ motion margins from daily images. Our results indicate that the developed tool can provide improved visualization and quantification of daily deformations for estimating isotropic and anisotropic margins for radiotherapy treatment plans in cases where organ motion is an issue such as the presented example of stomach lymphoma, or in cases of lung and prostate cancers.