TU‐EE‐A1‐05: Exploring the Spatial Trade‐Off in Treatment Planning

Abstract

Purpose: To include spatial information during multi‐criteria treatment planning. Particularly, to study whether constrained optimization on the voxel level allows to deliberately trade‐off the dose delivered to one region of a volume of interest (VOI) with respect to other clinical goals. Method and Materials: We extended a stepwise optimization method for robotic radiosurgery to interactively modify dose constraints on a voxel level. The optimization problem is solved using linear programming, and every term in the objective function is matched by a corresponding constraint. Clinical goals are addressed separately and maintained using the constraints. A trade‐off among the clinical goals is then explored by a series of optimization steps. For visualization, VOIs are represented by a 3D grid of spheres, where each sphere represents a voxel and can be selected in a 3D scene. Constraints on the dose in the selected voxels can be considered independently for subsequent optimization steps. The method was applied to a prostate case, where we studied trade‐offs with respect to the maximum dose in the rectum. Results: Relaxing the upper dose bound on a set of voxels in the prostate lobes by 150 cGy allowed to reduce the maximum rectum dose by 100 cGy. Likewise, a relaxation of the lower dose bound on a few voxels on the prostate surface by 100 cGy allowed to further reduce the maximum dose in th rectum by 157 cGy. Conclusion: Spatial information is not available from cumulative statistics typically used as criteria for treatment planning. Our results indicate that it is possible to include spatial information in interactive multi‐criteria optimization. The proposed method can be used when clinical goals can be expressed with respect to a subregion of a VOI. Conflict of Interest: Research partially sponsored by Accuray Inc.