MO-FG-BRA-08: Swarm Intelligence-Based Personalized Respiratory Gating in Lung SAbR

Authors


Abstract

Purpose:

Respiratory gating is widely deployed as a clinical motion-management strategy in lung radiotherapy. In conventional gating, the beam is turned on during a pre-determined phase window; typically, around end-exhalation. In this work, we challenge the notion that end-exhalation is always the optimal gating phase. Specifically, we use a swarm-intelligence-based, inverse planning approach to determine the optimal respiratory phase and MU for each beam with respect to (i) the state of the anatomy at each phase and (ii) the time spent in that state, estimated from long-term monitoring of the patient's breathing motion.

Methods:

In a retrospective study of five lung cancer patients, we compared the dosimetric performance of our proposed personalized gating (PG) with that of conventional end-of-exhale gating (CEG) and a previously-developed, fully 4D-optimized plan (combined with MLC tracking delivery). For each patient, respiratory phase probabilities (indicative of the time duration of the phase) were estimated over 2 minutes from lung tumor motion traces recorded previously using the Synchrony system (Accuray Inc.). Based on this information, inverse planning optimization was performed to calculate the optimal respiratory gating phase and MU for each beam. To ensure practical deliverability, each PG beam was constrained to deliver the assigned MU over a time duration comparable to that of CEG delivery.

Results:

Maximum OAR sparing for the five patients achieved by the PG and the 4D plans compared to CEG plans was: Esophagus Dmax [PG:57%, 4D:37%], Heart Dmax [PG:71%, 4D:87%], Spinal cord Dmax [PG:18%, 4D:68%] and Lung V13 [PG:16%, 4D:31%]. While patients spent the most time in exhalation, the PG-optimization chose end-exhale only for 28% of beams.

Conclusion:

Our novel gating strategy achieved significant dosimetric improvements over conventional gating, and approached the upper limit represented by fully 4D optimized planning while being significantly simpler and more clinically translatable.

This work was partially supported through research funding from National Institutes of Health (R01CA169102) and Varian Medical Systems, Palo Alto, CA, USA.

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