SU-E-T-609: Improving the Efficiency of VMAT Plan Optimization by Using Sparse Decomposition Method

Medical Physics
Yong Hum NaLei Xing

Abstract

Volumetric modulated arc therapy (VMAT) is capable of delivering highly conformable dose distribution efficiently. Its planning is, however, more computationally intensive and requires a huge amount of memory space for optimization. We present an efficient sparse decomposition method for VMAT plan optimization. A quadratic objective function with volumetric constraints is expressed as a function of the aperture shapes and weights of the incident beams. The algorithm generates a sequence of iterates to solve the optimization problem. Each step of iteratively reweighed method is to be updated by solving the subproblem involving a quadratic (L2) term and a sparsity-inducing regulation (L1) term. Through the sparse decomposition techniques of the given problem, the deliverable apertures are directly generated. The shape of each aperture is iteratively rectified to be a sequencing of arc using the manufacture constraints. An initial arc spacing of 8 degree creates 45 beams directions for a single arc, 360 degree. The angular separation is equispaced every 2 degree over the end of optimization cycle. The optimization is implemented for a Varian TrueBeamTM STX linac beams with and without flattening filters available. Three clinical c...Continue Reading

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