Multicriteria VMAT optimization

TL;DR

This paper introduces VMERGE, a multicriteria optimization algorithm for VMAT planning that rapidly generates high-quality, deliverable plans by navigating the Pareto surface and merging fluence maps, enabling efficient tradeoff exploration.

Contribution

The paper presents a novel convex multicriteria dose optimization method combined with a fluence map merging algorithm, significantly improving planning speed and flexibility over existing VMAT techniques.

Findings

High-quality plans delivered in under five minutes.

Effective tradeoff exploration between plan quality and delivery time.

Significant improvements over existing VMAT algorithms.

Abstract

Purpose: To make the planning of volumetric modulated arc therapy (VMAT) faster and to explore the tradeoffs between planning objectives and delivery efficiency. Methods: A convex multicriteria dose optimization problem is solved for an angular grid of 180 equi-spaced beams. This allows the planner to navigate the ideal dose distribution Pareto surface and select a plan of desired target coverage versus organ at risk sparing. The selected plan is then made VMAT deliverable by a fluence map merging and sequencing algorithm, which combines neighboring fluence maps based on a similarity score and then delivers the merged maps together, simplifying delivery. Successive merges are made as long as the dose distribution quality is maintained. The complete algorithm is called VMERGE. Results: VMERGE is applied to three cases: a prostate, a pancreas, and a brain. In each case, the selected Pareto-optimal plan is matched almost exactly with the VMAT merging routine, resulting in a high quality plan delivered with a single arc in less than five minutes on average. VMERGE offers significant improvements over existing VMAT algorithms. The first is the multicriteria planning aspect, which greatly speeds up planning time and allows the user to select the plan which represents the most desirable compromise between target coverage and organ at risk sparing. The second is the user-chosen epsilon-optimality guarantee of the final VMAT plan. Finally, the user can explore the tradeoff between delivery time and plan quality, which is a fundamental aspect of VMAT that cannot be easily investigated with current commercial planning systems.