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

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 cases, head and neck, lung, and prostate, have been studied for the purpose of evaluating the planning efficiency and quality of the plans.

The target dose coverage and critical structure sparing of VMAT plan are comparable to those of IMRT plans. The VMAT plan delivers lower doses to other OARs while keeping the similar target dose coverage to IMRT plan. The VMAT plan optimizations takes less than 3 minutes on average of the cases indicating great efficiency compared to existing methods.

The results demonstrate that the proposed method provides competent computational efficiency for optimizing VMAT plan. The method substantially improves the speed and accuracy of VMAT plan optimization and makes future on-treatment adaptive re-planning possible.