Program Information
Fluence Map Optimization Using a Voxel-Based Reduced-Order Constrained Optimization Model
H Nourzadeh1*, R Radke2 , A Jackson3 , (1) Rensselaer Polytechnic Institute, Troy, NY, (2) Rensselaer Polytechnic Institute, Troy, NY, (3) Memorial Sloan-Kettering Cancer Center, New York, NY
Presentations
TH-EF-BRD-1 (Thursday, July 16, 2015) 1:00 PM - 2:50 PM Room: Ballroom D
Purpose:
To develop a method for adaptively imposing dose constraints on individual voxels and structures in IMRT planning, such that a sequence of computationally tractable, low-dimensional constrained optimization problems is solved that leads to plans satisfying dose-volume and other clinical constraints.
Methods:
The previously proposed Reduced Order Constrained Optimization (ROCO) algorithm is applied to parameterize an IMRT problem by a small number of coefficients on learned principal components, instead of a large number of independent beamlet fluences. A sequence of constrained optimization problems is solved over the set of coefficients in which the importance of each voxel and structure is adaptively changed in the objective function. Instead of manually investigating good settings for these parameters, an adaptive gain scheduling rule based on control theory automatically adjusts the weights, quickly converging to a plan satisfying the clinical constraints. In particular, we apply a decentralized proportional-integral controller that adjusts structure weights using dose-volume constraint (DVC) violation feedback. We compare our method against a previous approach in which subsets of voxels were iteratively selected and hard-constrained in an attempt to satisfy DVCs.
Results:
The technique was tested on five different non-nodal prostate cases, each resulting in clinically acceptable plans in less than 2.5 minutes on average. The quality of plans is far superior to the alternate method, significantly reducing OARs doses while meeting PTV V95 goals.
Conclusion:
Compared to alternative optimization schemes in the full intensity space, the propose scheme offers fast convergence rate, demands significantly less memory resources and computational effort, which allows larger IMRT problems to be addressed.
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