Program Information
Solving Larger IMRT Problems by Enhanced Reduced-Order Constrained Optimization
H Nourzadeh1*, R Radke2 , A Jackson3 , S Bakr4 , S Tuomaala5 , (1) Rensselaer Polytechnic Institute, Troy, NY, (2) Rensselaer Polytechnic Institute, Troy, NY, (3) Mem Sloan-Kettering Cancer Ctr, New York, NY, (4) Memorial Solan-Kettering Cancer Center, New York, New York, (5) Varian Medical System, Palo Alto, CA
Presentations
TH-AB-BRB-1 (Thursday, July 16, 2015) 7:30 AM - 9:30 AM Room: Ballroom B
Purpose:
To report the latest improvements in the pipeline of the reduced-order constrained optimization (ROCO) method for fast IMRT planning.
Methods:
The ROCO method involves the characterization of the solution space of the underlying fluence map IMRT optimization problem in a compact form, which enables forming a clinically tractable constrained optimization problem. The offline part of the method consists of three stages: sampling, learning the effective mode space, and mode-dose calculation. In the sampling stage, a set of unconstrained optimization problems is solved, each corresponding to a different choice of weights and dose-volume limits. As opposed to previous implementations, we preprocess the samples, identify the effective beamlets with non-zero variances, and use them to attain a compact representation of the fluence space using Principal Component Analysis (PCA). In the mode-dose calculation stage, the dose corresponding to each mode is computed based on the underlying full dose-calculation technique. We offer a new formulation for handling possible negative mode elements, exploiting linearity of the mode-dose map. In the online phase, a constrained optimization problem with a minimal number of constraints is solved over the basis coefficients spanning the reduced-size space.
Results:
The results suggest that for a typical prostate case, the PCA stage can be performed about 5 times faster. Moreover, the mode dose calculation performance is accelerated by a factor of 2. Apart from computational efficiency, we also observed significantly lower memory demand for ROCO model compared to optimization models relying on pre-calculation of dose deposition coefficients (DDCs). The proposed method has been integrated into the Varian Eclipse version 13.5 treatment planning system as a stand-alone application.
Conclusion:
The new implementation has led a substantial savings in memory requirements and computational effort, allowing larger IMRT problems with finer beamlet resolution to be addressed with the available memory resources in less time.
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