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. 2019 Apr;46(4):1814-1820.
doi: 10.1002/mp.13425. Epub 2019 Mar 4.

Technical Note: In silico and experimental evaluation of two leaf-fitting algorithms for MLC tracking based on exposure error and plan complexity

Vincent Caillet  1   2 , Ricky O'Brien  2 , Douglas Moore  3 , Per Poulsen  4 , Tobias Pommer  5 , Emma Colvill  1   2 , Amit Sawant  6 , Jeremy Booth  1   2 , Paul Keall  2
Affiliations

Technical Note: In silico and experimental evaluation of two leaf-fitting algorithms for MLC tracking based on exposure error and plan complexity

Vincent Caillet et al. Med Phys. 2019 Apr.

Abstract

Purpose: Multileaf collimator (MLC) tracking is being clinically pioneered to continuously compensate for thoracic and pelvic motion during radiotherapy. The purpose of this work was to characterize the performance of two MLC leaf-fitting algorithms, direct optimization and piecewise optimization, for real-time motion compensation with different plan complexity and tumor trajectories.

Methods: To test the algorithms, both in silico and phantom experiments were performed. The phantom experiments were performed on a Trilogy Varian linac and a HexaMotion programmable motion platform. 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 leaf-fitting algorithms, the plans were run with their corresponding patient trajectories. To compare algorithms, the average exposure errors, i.e., the difference in shape between ideal and fitted MLC leaves by the algorithm, plan complexity and system latency of each experiment were calculated.

Results: Comparison of exposure errors for the in silico and phantom experiments showed minor differences between the two algorithms. The average exposure errors for in silico experiments with low/high plan complexity were 0.66/0.88 cm2 for direct optimization and 0.66/0.88 cm2 for piecewise optimization, respectively. The average exposure errors for the phantom experiments with low/high plan complexity were 0.73/1.02 cm2 for direct and 0.73/1.02 cm2 for piecewise optimization, respectively. The measured latency for the direct optimization was 226 ± 10 ms and for the piecewise algorithm was 228 ± 10 ms. In silico and phantom exposure errors quantified for each treatment plan demonstrated that the exposure errors from the high plan complexity (0.96 cm2 mean, 2.88 cm2 95% percentile) were all significantly different from the low plan complexity (0.70 cm2 mean, 2.18 cm2 95% percentile) (P < 0.001, two-tailed, Mann-Whitney statistical test).

Conclusions: The comparison between the two leaf-fitting algorithms demonstrated no significant differences in exposure errors, neither in silico nor with phantom experiments. This study revealed that plan complexity impacts the overall exposure errors significantly more than the difference between the algorithms.

Keywords: MLC tracking; fitting algorithm; radiotherapy; real-time.

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Conflict of interest statement

Paul J Keall and Jeremy Booth are investigators on one completed and two ongoing MLC tracking clinical trials that have been partially supported by Varian Medical Systems. Paul J Keall and Amit Sawant are inventors on one licensed patent and one unlicensed patent related to MLC tracking. Paul J Keall, Ricky O'Brien, and Vincent Caillet gratefully acknowledge funding from the Australian Cancer Research foundation. Paul J Keall acknowledges funding from an Australian Government NHMRC Senior Principal Research Fellowship.

Figures

Figure 1
Figure 1
Performance of each algorithm was characterized by two sets of experiments, in silico and phantom, conducted for two specific target scenarios (lung and prostate) combining different sets of plan complexities (high and low) and trajectories (baseline shift, high frequency, etc.). The exposure errors were calculated for each scenario. [Color figure can be viewed at wileyonlinelibrary.com]
Figure 2
Figure 2
For each scenario, the exposure errors were compared for each set of paired experiments to compare the piecewise algorithm against the direct optimization.
Figure 3
Figure 3
Leaf‐fitting exposure errors for the direct (gray) and piecewise (red) optimization for both in silico and phantom experiment (delivered). The Pearson correlation coefficient (r) and the root‐mean‐square error are provided for each paired experiment showing that the sum of exposure errors is equivalent given any tumor motion, organ, and plan complexity.
See this image and copyright information in PMC

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