Technical Note: In silico and experimental evaluation of two leaf‐fitting algorithms for MLC tracking based on exposure error and plan complexity. Issue 4 (4th March 2019)

• Record Type: Journal Article
• Authors: Caillet, Vincent; O'Brien, Ricky; Moore, Douglas; Poulsen, Per; Pommer, Tobias; Colvill, Emma; Sawant, Amit; Booth, Jeremy; Keall, Paul
• Is Part Of: Medical physics. Volume 46:Issue 4(2019)
• Page Start: 1814
• Page End: 1820
• Publication Date: 2019-03-04
• Subjects: fitting algorithm -- MLC tracking -- radiotherapy -- real‐time; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.13425
• Languages: English
• ISSNs: 0094-2405

Abstract

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 cm 2 for direct optimization and 0.66/0.88 cm 2 for piecewise optimization, respectively. The average exposure errors for the phantom experiments with low/high plan complexity were 0.73/1.02 cm 2 for direct and 0.73/1.02 cm 2 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 cm 2 mean, 2.88 cm 2 95% percentile) were all significantly different from the low plan complexity (0.70 cm 2 mean, 2.18 cm 2 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.