  Noncoplanar radiation therapy treatment planning has the potential to improve
dosimetric quality as compared to traditional coplanar techniques. Likewise,
automated treatment planning algorithms can reduce a planner's active treatment
planning time and remove inter-planner variability. To address the limitations
of traditional treatment planning, we have been developing a suite of
algorithms called station parameter optimized radiation therapy (SPORT). Within
the SPORT suite of algorithms, we propose a method called NC-POPS to produce
noncoplanar (NC) plans using the fully automated Pareto Optimal Projection
Search (POPS) algorithm. Our NC-POPS algorithm extends the original POPS
algorithm to the noncoplanar setting with potential applications to both IMRT
and VMAT. The proposed algorithm consists of two main parts: 1) noncoplanar
beam angle optimization (BAO) and 2) fully automated inverse planning using the
POPS algorithm. We evaluate the performance of NC-POPS by comparing between
various noncoplanar and coplanar configurations. To evaluate plan quality, we
compute the homogeneity index (HI), conformity index (CI), and dose-volume
histogram (DVH) statistics for various organs-at-risk (OARs). As compared to
the evaluated coplanar baseline methods, the proposed NC-POPS method achieves
significantly better OAR sparing, comparable or better dose conformity, and
similar dose homogeneity. Our proposed NC-POPS algorithm provides a modular
approach for fully automated treatment planning of noncoplanar IMRT cases with
the potential to substantially improve treatment planning workflow and plan
quality.
