Program Information
Head-To-Head Performance Characterization of Two Multileaf Collimator Tracking Algorithms for Radiotherapy
V Caillet1,2*, R O'Brien1 , E Colvill1,2 , P Poulsen3 , D Moore4,5 , J Booth2 , A Sawant5 , P Keall1 , (1) School of Medecine, The University of Sydney, Sydney, NSW, (2) Royal North Shore Hospital, St Leonards, Sydney, (3) Aarhus University Hospital, Aarhus, (4) UT Southwestern Medical Center, Dallas, TX, (5) University of Maryland School of Medicine, Baltimore, MD
Presentations
SU-G-JeP1-12 (Sunday, July 31, 2016) 4:00 PM - 4:30 PM Room: ePoster Theater
Purpose: Multi-leaf collimator (MLC) tracking is being clinically pioneered to continuously compensate for thoracic and abdominal motion during radiotherapy. The purpose of this work is to characterize the performance of two MLC tracking algorithms for cancer radiotherapy, based on a direct optimization and a piecewise leaf fitting approach respectively.
Methods: To test the algorithms, both physical and in silico experiments were performed. Previously published high and low modulation VMAT plans for lung and prostate cancer cases were used along with eight patient-measured organ-specific trajectories. For both MLC tracking algorithm, the plans were run with their corresponding patient trajectories. The physical experiments were performed on a Trilogy Varian linac and a programmable phantom (HexaMotion platform). For each MLC tracking algorithm, plan and patient trajectory, the tracking accuracy was quantified as the difference in aperture area between ideal and fitted MLC. To compare algorithms, the average cumulative tracking error area for each experiment was calculated. The two-sample Kolmogorov-Smirnov (KS) test was used to evaluate the cumulative tracking errors between algorithms.
Results: Comparison of tracking errors for the physical and in silico experiments showed minor differences between the two algorithms. The KS D-statistics for the physical experiments were below 0.05 denoting no significant differences between the two distributions pattern and the average error area (direct optimization/piecewise leaf-fitting) were comparable (66.64 cm2/65.65 cm2). For the in silico experiments, the KS D-statistics were below 0.05 and the average errors area were also equivalent (49.38 cm2/48.98 cm2).
Conclusion: The comparison between the two leaf fittings algorithms demonstrated no significant differences in tracking errors, neither in a clinically realistic environment nor in silico. The similarities in the two independent algorithms give confidence in the use of either algorithm for clinical implementation.
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