(Wednesday, 8/1/2018) 1:45 PM - 3:45 PM
Room: Davidson Ballroom A
Purpose: To incorporate plan robustness as a sensitivity term in Intensity-Modulated Proton Therapy (IMPT) optimization while simultaneously maximizing the dose fidelity.
Methods: The sensitivity-regularized robust optimization framework is formulated with a dose fidelity term and a robustness regularization term, which penalizes the inner product of the scanning-spot sensitivity and intensity. The sensitivity of an IMPT scanning-spot to perturbations is defined as the dose distribution variation induced by range and positioning uncertainties. To evaluate the sensitivity, the spatial gradient of the dose distribution of a specific spot is first calculated. The gradient is then divided to the directions parallel and perpendicular to the proton beam. The total absolute value of the directional gradients of all affected voxels quantifies the spot sensitivity. The Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) was used to solve the optimization problem. This method was tested on one skull-base chordoma patient and two bilateral head-and-neck patients. It was compared against conventional PTV-based optimization method and minimax approach, which is also known as the worst-case optimization.
Results: Under the nominal situation without uncertainties, the three methods achieve similar CTV dose coverage, while the proposed approach has better OAR sparing compared with the minimax approach, with an average reduction of [Dmean, Dmax] of [1.84, 1.36] GyRBE. The proposed approach and minimax method both improve the plan robustness from conventional PTV-based plans, increasing the average lowest D95% of CTV from 92.87%, to 96.86% and 96.93% of prescription dose, respectively. And on average our method reduces the runtime from the minimax method by approximately a factor of 5.
Conclusion: We developed a novel computationally efficient robust optimization method for IMPT, which is shown to achieve comparable robustness as the worst-case approach, yet markedly improved the dose fidelity and OAR sparing.
Funding Support, Disclosures, and Conflict of Interest: NIH U19AI067769 DE-SC0017687 NIH R21CA228160 DE-SC0017057 NIH R44CA183390 NIH R43CA183390 NIH R01CA188300
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