Program Information
Fast, Deterministic Leaf-Fitting with Explicit Underdose/overdose Constraints for Real-Time MLC Tracking
D Moore* and A Sawant , UT Southwestern Medical Center, Dallas, TX
Presentations
TH-AB-303-5 (Thursday, July 16, 2015) 7:30 AM - 9:30 AM Room: 303
Purpose:
Real-time motion management via dynamic MLC tracking requires adaptive aperture reshaping and fast leaf-fitting while still attempting to preserve the dosimetric integrity of the treatment. Due to the finite width of MLC leaves, the reshaped aperture can only be approximated by leaf refitting. A previously proposed algorithm by Ruan (2011) successfully minimized the refitting error by utilizing explicit underdose/overdose constraints to find an optimal leaf configuration for a given aperture. In its current implementation, this algorithm is computationally- and therefore time-intensive, which is a concern for real-time MLC tracking. In this work, we propose a significantly faster version of this algorithm that takes advantage of the polygonal characteristics of plan-derived apertures.
Methods:
Two hundred distinct leaf configurations were extracted from two conformal and three sliding-window lung IMRT plans. Twenty predetermined affine transformations were applied to each aperture to generate 4000 test shapes. Both algorithms, our piecewise approach and Ruan (2011), were then applied to each shape using the same set of ten underdose/overdose coefficients. Performance statistics, including run time and memory consumption, were gathered during testing. The resulting leaf positions as well as the overall fitting cost of each algorithm were compared for each shape.
Results:
Our piecewise approach reduces runtime from an average of 113ms to 0.295ms per aperture. The original algorithm showed the most consistent performance with minimum and maximum runtimes of 83.6ms and 129ms, respectively, compared to 0.104ms and 0.848ms with our approach. In both cases, memory usage was negligible, averaging 312kb for Ruan (2011) and 130kb for our approach.
Conclusion:
Our piecewise leaf-fitting approach showed three orders of magnitude improvement in computation time compared to the original implementation. Such improvement has a direct impact on system latency (time between target motion and MLC response) and therefore on the geometric and dosimetric accuracy of MLC tracking.
Funding Support, Disclosures, and Conflict of Interest: Funding for this work was provided by Elekta Limited.
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