  Manual forward planning for GK radiosurgery is complicated and
time-consuming, particularly for cases with large or irregularly shaped
targets. Inverse planning eases GK planning by solving an optimization problem.
However, due to the vast search space, most inverse planning algorithms have to
decouple the planning process to isocenter preselection and sector duration
optimization. This sequential scheme does not necessarily lead to optimal
isocenter locations and hence optimal plans. In this study, we attempt to
optimize the isocenter positions, beam shapes and durations simultaneously by
proposing a multi-resolution-level (MRL) strategy to handle the large-scale GK
optimization problem. In our approach, several rounds of optimizations were
performed with a progressively increased spatial resolution for isocenter
candidate selection. The isocenters selected from last round and their
neighbors on a finer resolution were used as new isocenter candidates for next
round of optimization. After plan optimization, shot sequencing was performed
to group the optimized sectors to deliverable shots supported by GK treatment
units. We have tested our algorithm on 6 GK cases previously treated in our
institution (2 meningioma cases, 3 cases with single metastasis and 1 case with
6 metastases). Compared with manual planning, achieving same coverage and
similar selectivity, our algorithm improved the gradient index averagely from
3.1 to 2.9 and reduced the maximum dose of brainstem from 8.0Gy to 5.6Gy. The
average beam-on time was also reduced by from 103.8 mins to 87.4 mins. Our
method was also compared with the inverse planning algorithm provided in
Leksell GammaPlan planning system, and outperformed it with better plan quality
for all the 6 cases.This preliminary study has demonstrated the effectiveness
and feasibility of our MRL inverse planning approach for GK radiosurgery.
