A novel reduced-order prioritized optimization method for radiation therapy treatment planning

Pareto Optimal Projection Search (POPS): Automated Radiation Therapy Treatment Planning by Direct Search of the Pareto Surface

OBJECTIVE

Radiation therapy treatment planning is a time-consuming, iterative process with potentially high inter-planner variability. Fully automated treatment planning processes could reduce a planner's active treatment planning time and remove inter-planner variability, with the potential to tremendously improve patient turnover and quality of care. In developing fully automated algorithms for treatment planning, we have two main objectives: to produce plans that are 1) Pareto optimal and 2) clinically acceptable. Here, we propose the Pareto optimal projection search (POPS) algorithm, which provides a general framework for directly searching the Pareto front.

METHODS

Our POPS algorithm is a novel automated planning method that combines two main search processes: 1) gradient-free search in the decision variable space and 2) projection of decision variables to the Pareto front using the bisection method. We demonstrate the performance of POPS by comparing with clinical treatment plans. As one possible quantitative measure of treatment plan quality, we construct a clinical acceptability scoring function (SF) modified from the previously developed general evaluation metric (GEM).

RESULTS

On a dataset of 21 prostate cases collected as part of clinical workflow, our proposed POPS algorithm produces Pareto optimal plans that are clinically acceptable in regards to dose conformity, dose homogeneity, and sparing of organs-at-risk.

CONCLUSION

Our proposed POPS algorithm provides a general framework for fully automated treatment planning that achieves clinically acceptable dosimetric quality without requiring active planning from human planners.

SIGNIFICANCE

Our fully automated POPS algorithm addresses many key limitations of other automated planning approaches, and we anticipate that it will substantially improve treatment planning workflow.

A simple and robust trajectory-based stereotactic radiosurgery treatment

INTRODUCTION

We present a Trajectory-based Volumetric Modulated Arc Therapy (TVMAT) technique for Stereotactic Radiosurgery (SRS) that takes advantage of a modern linacs ability to modulate dose rate and move the couch dynamically. In addition, we investigate the quality of the developed TVMAT method and the dosimetric accuracy of the technique.

METHODS

The main feature of the TVMAT technique is a standard beam trajectory formed by dynamic motion of the treatment couch and the linac gantry. The couch rotates slowly through 180 degrees while the gantry delivers radiation through continuous sweeps of the gantry. The number of partial arcs that constitute the trajectory can be varied between two and eight and as the number of partial arcs increases, the trajectory more finely samples 4π geometry. Along these trajectories, the multi-leaf collimator (MLC) and dose rate are optimized through an inverse planning framework. The TVMAT method was tested on ten cranial SRS patients who were previously treated with the Dynamic Conformal Arc (DCA) technique. The plans were compared with the DCA and a four- arc VMAT technique with regards to dose to the OAR, dose falloff, V12Gy, and V4Gy. Validation measurements were performed using ion-chamber and Gafchromic film. In addition, the trajectory-log files were analyzed and compared with the treatment plan beam data.

RESULTS

The TVMAT treatment plans were successfully delivered with a treatment time between 3-8 min which mostly depended on total cumulated dose. Ion chamber measurements had an average measured error of 1.1 ± 0.6% and a maximum value of 2.2% of the delivered dose. The 2%, 2 mm gamma pass rates for the film measurements were 96% or greater. In a preliminary comparison of ten patients who underwent SRS treatments with the DCA technique, the TVMAT and VMAT techniques were able to produce plans with comparable dose falloff and OAR doses, while achieving better dose conformality, V4Gy and V12Gy when compared to the original DCA plans. The improvement of the TVMAT plans were as follows (mean % improvement ± standard err): Conformity (10 ± 2%), V4 (20 ± 20%), V12 (27 ± 10%), volume weighted mean dose to organs at risk (13 ± 13%), homogeneity index (2 ± 2%) and falloff (4 ± 2%).

CONCLUSION

We have developed and validated a trajectory-based dose delivery method which has dose distribution improvements while having a treatment time of 3-8 min. In addition, it has the potential for a simpler planning experience while maintaining an accurate delivery on the Varian Truebeam Linac.

Radiotherapy Planning Using an Improved Search Strategy in Particle Swarm Optimization

OBJECTIVE

Evolutionary stochastic global optimization algorithms are widely used in large-scale, nonconvex problems. However, enhancing the search efficiency and repeatability of these techniques often requires well-customized approaches. This study investigates one such approach.

METHODS

We use particle swarm optimization (PSO) algorithm to solve a 4D radiation therapy (RT) inverse planning problem, where the key idea is to use respiratory motion as an additional degree of freedom in lung cancer RT. The primary goal is to administer a lethal dose to the tumor target while sparing surrounding healthy tissue. Our optimization iteratively adjusts radiation fluence-weights for all beam apertures across all respiratory phases. We implement three PSO-based approaches: conventionally used unconstrained, hard-constrained, and our proposed virtual search. As proof of concept, five lung cancer patient cases are optimized over ten runs using each PSO approach. For comparison, a dynamically penalized likelihood (DPL) algorithm-a popular RT optimization technique is also implemented and used.

RESULTS

The proposed technique significantly improves the robustness to random initialization while requiring fewer iteration cycles to converge across all cases. DPL manages to find the global optimum in 2 out of 5 RT cases over significantly more iterations.

CONCLUSION

The proposed virtual search approach boosts the swarm search efficiency, and consequently, improves the optimization convergence rate and robustness for PSO.

SIGNIFICANCE

RT planning is a large-scale, nonconvex optimization problem, where finding optimal solutions in a clinically practical time is critical. Our proposed approach can potentially improve the optimization efficiency in similar time-sensitive problems.