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Integrated Beam Angle and Scanning-Spot Optimization in Intensity Modulated Proton Therapy Using Group Sparsity

W Gu

W Gu*, D O'Connor , V Yu , D Nguyen , D Ruan , K Sheng , UCLA School of Medicine, Los Angeles, CA


TH-AB-605-7 (Thursday, August 3, 2017) 7:30 AM - 9:30 AM Room: 605

Purpose: To develop a novel integrated beam angle optimization (BAO) and scanning-spot optimization algorithm for intensity modulated proton therapy (IMPT).

Methods: 1162 non-coplanar candidate beams evenly distributed across 4π steradians were included in the optimization. For each candidate beam, the pencil-beam doses of all scanning-spots covering the PTV and a margin were calculated. The beam angle selection and spot intensity optimization problem was formulated to include three terms: an L2-norm dose fidelity term to penalize the deviation of PTV and OAR doses from prescription; an L1-norm sparsity term to reduce the number of active spots and improve delivery efficiency; a group sparsity term to minimize the number of active beams to between 2 and 4. For the group sparsity term, convex L2,1-norm and nonconvex L2,½-norm were tested . The optimization problem was solved using the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). The BAO-IMPT algorithm was tested on a GBM and a head-and-neck patient. The results were compared with IMPT plans using manually selected beams.

Results: Two beams for the GBM patient and three beams for the H&N patient were selected and optimized using BAO-IMPT in 20 minutes on an i7-core desktop. The L2,1-norm plan selected spatially aggregated beams, indicating potential degeneracy using this norm. L2,½-norm was able to select spatially separated beams. In addition to beam selection, excellent spot sparsity was achieved with only 10% to 20% of candidate spots active. In the L2,1/2-norm plans, the mean OAR dose was reduced by an average 20.2% compared with the IMPT plan using manual beam selection while maintaining the same PTV coverage.

Conclusion: This work shows the first IMPT approach integrating BAO and scanning-spot optimization in a single mathematical framework. This method is computationally fast, dosimetrically superior and produces delivery-efficient IMPT plans. This method will be further developed to incorporate robust planning.

Funding Support, Disclosures, and Conflict of Interest: DOE DE-SC0017057 NIH R44CA183390 NIH R01CA188300 NIH R43CA183390 NIH U19AI067769

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