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Investigation of probabilistic optimization for tomotherapy
Journal article   Open access   Peer reviewed

Investigation of probabilistic optimization for tomotherapy

Michael W Kissick, Thomas R Mackie, Ryan T Flynn, Xiaohu Mo, David D Campos, Yue Yan and Donghui Zhao
Journal of applied clinical medical physics, Vol.13(5), pp.3865-3865
09/2012
DOI: 10.1120/jacmp.v13i5.3865
PMCID: PMC3753820
PMID: 22955654
url
https://doi.org/10.1120/jacmp.v13i5.3865View
Published (Version of record) Open Access

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.
tumor motion interplay IMRT robust optimization

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