Investigation of probabilistic optimization for tomotherapy

Abstract

This work builds on a suite of studies related to the 'interplay', or lack thereof, for respiratory motion with helical tomotherapy (HT). It helps explain why HT treatments without active motion management had clinical outcomes that matched positive expectations. An analytical calculation is performed to illuminate the frequency range for which interplay-type dose errors could occur. Then, an experiment is performed which completes a suite of tests. The experiment shows the potential for a stable motion probability distribution function (PDF) with HT and respiratory motion. This PDF enables one to use a motion-robust or probabilistic optimization to intrinsically include respiratory motion into the treatment planning. The reason why HT is robust to respiratory motion is related to the beam modulation sampling of the tumor motion. Because active tracking-based motion management is more complicated for a variety of reasons, HT optimization that is robust to motion is a useful alternative for those many patients that cannot benefit from active motion management.