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- W1470036381 abstract "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." @default.
- W1470036381 created "2016-06-24" @default.
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- W1470036381 date "2015-06-01" @default.
- W1470036381 modified "2023-09-27" @default.
- W1470036381 title "TH-AB-BRB-01: Solving Larger IMRT Problems by Enhanced Reduced-Order Constrained Optimization" @default.
- W1470036381 doi "https://doi.org/10.1118/1.4926132" @default.
- W1470036381 hasPublicationYear "2015" @default.
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