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
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.