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Plan Averaging for Multi-Criteria Navigation of Step-And-Shoot IMRT

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M Guo

M Guo1*, D Craft2 , H Gao3 , (1) Shanghai Jiao Tong University, Shanghai, Shanghai, (2) Massachusetts General Hospital, Cambridge, MA, (3) Shanghai Jiao Tong University, Shanghai, Shanghai


SU-E-T-614 (Sunday, July 12, 2015) 3:00 PM - 6:00 PM Room: Exhibit Hall

Purpose:Step-and-shoot IMRT is fundamentally discrete in nature, while multi-criteria optimization (MCO) is fundamentally continuous: the MCO planning consists of continuous sliding across the Pareto surface (the set of plans which represent the tradeoffs between organ-at-risk doses and target doses). In order to achieve close to real-time dose display during this sliding, it is desired that averaged plans share many of the same apertures as the pre-computed plans, since dose computation for apertures generated on-the-fly would be expensive. We propose a method to ensure that neighboring plans on a Pareto surface share many apertures.

Methods:Our baseline step-and-shoot sequencing method is that of K. Engel (a method which minimizes the number of segments while guaranteeing the minimum number of monitor units), which we customize to sequence a set of Pareto optimal plans simultaneously. We also add an error tolerance to study the relationship between the number of shared apertures, the total number of apertures needed, and the quality of the fluence map re-creation.

Results:We run tests for a 2D Pareto surface trading off rectum and bladder dose versus target coverage for a clinical prostate case. We find that if we enforce exact fluence map recreation, we are not able to achieve much sharing of apertures across plans. The total number of apertures for all seven beams and 4 plans without sharing is 217. With sharing and a 2% error tolerance, this number is reduced to 158 (73%).

Conclusion:With the proposed method, total number of apertures can be decreased by 42% (averaging) with no increment of total MU, when an error tolerance of 5% is allowed. With this large amount of sharing, dose computations for averaged plans which occur during Pareto navigation will be much faster, leading to a real-time what-you-see-is-what-you-get Pareto navigation experience.

Funding Support, Disclosures, and Conflict of Interest: Minghao Guo and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000) and the Shanghai Pujiang Talent Program (#14PJ1404500).

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