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Group-Sparsity Based Angle Generation Method for Beam Angle Optimization

H Gao

H Gao1*, (1) Shanghai Jiao Tong University, Shanghai, Shanghai


SU-E-T-446 (Sunday, July 12, 2015) 3:00 PM - 6:00 PM Room: Exhibit Hall

Purpose: This work is to develop the effective algorithm for beam angle optimization (BAO), with the emphasis on enabling further improvement from existing treatment-dependent templates based on clinical knowledge and experience.

Methods: The proposed BAO algorithm utilizes a priori beam angle templates as the initial guess, and iteratively generates angular updates for this initial set, namely angle generation method, with improved dose conformality that is quantitatively measured by the objective function. That is, during each iteration, we select "the test angle" in the initial set, and use group-sparsity based fluence map optimization to identify "the candidate angle" for updating "the test angle", for which all the angles in the initial set except "the test angle", namely "the fixed set", are set free, i.e., with no group-sparsity penalty, and the rest of angles including "the test angle" during this iteration are in "the working set". And then "the candidate angle" is selected with the smallest objective function value from the angles in "the working set" with locally maximal group sparsity, and replaces "the test angle" if "the fixed set" with "the candidate angle" has a smaller objective function value by solving the standard fluence map optimization (with no group-sparsity regularization). Similarly other angles in the initial set are in turn selected as "the test angle" for angular updates and this chain of updates is iterated until no further new angular update is identified for a full loop.

Results: The tests using the MGH public prostate dataset demonstrated the effectiveness of the proposed BAO algorithm. For example, the optimized angular set from the proposed BAO algorithm was better the MGH template.

Conclusion: A new BAO algorithm is proposed based on the angle generation method via group sparsity, with improved dose conformality from the given template.

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

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