Guaranteed epsilon-optimal treatment plans with minimum number of beams for stereotactic body radiation therapy

TL;DR

This paper introduces a mixed integer programming method with heuristics to generate epsilon-optimal treatment plans with minimal beams for SBRT, balancing plan quality and delivery efficiency.

Contribution

The paper presents a novel optimization approach that guarantees epsilon-optimality while minimizing the number of beams, improving treatment plan quality and delivery time.

Findings

Effective reduction in the number of beams while maintaining plan quality.

Significant decrease in computation time using heuristics.

Validated on clinical liver and lung cases.

Abstract

Stereotactic body radiotherapy (SBRT) is characterized by delivering a high amount of dose in a short period of time. In SBRT the dose is delivered using open fields (e.g., beam's-eye-view) known as "apertures". Mathematical methods can be used for optimizing treatment planning for delivery of sufficient dose to the cancerous cells while keeping the dose to surrounding organs at risk (OARs) minimal. Two important elements of a treatment plan are quality and delivery time. Quality of a plan is measured based on the target coverage and dose to OARs. Delivery time heavily depends on the number of beams used in the plan since the setup times for different beam directions constitute a large portion of the delivery time. Therefore the ideal plan, in which all potential beams can be used simultaneously, will be associated with a long impractical delivery time. We use the dose to OARs in the ideal plan to find the plan with the minimum number of beams which is guaranteed to be epsilon-optimal (i.e., a predetermined maximum deviation from the ideal plan is guaranteed). Since the treatment plan optimization is inherently a multicriteria optimization problem, the planner can navigate the ideal dose distribution Pareto surface and select a plan of desired target coverage versus OARs sparing, and then use the proposed technique to reduce the number of beams while guaranteeing epsilon-optimality. We use mixed integer programming (MIP) for optimization. To reduce the computation time for the resultant MIP, we use two heuristics: a beam elimination scheme and a family of heuristic cuts, known as "neighbor cuts", based on the concept of "adjacent beams". We show the effectiveness of the proposed solution technique on two clinical cases, a liver and a lung case. Based on our technique we propose an algorithm for fast generation of epsilon-optimal plans.