Improved swarm intelligence solution in large scale radiation therapy inverse planning

This study employs particle swarm optimization to solve the non-convex inverse problem of 4D stereotactic body radiation therapy planning, targeting toxicity reduction, for a right lower lobe lung tumor with motion range of 1.5cm. A novel approach is introduced to reduce the swarm search space. 90 aperture-weights are optimized using both conventional and improved PSO algorithms over 5 optimization runs per method. It is shown that, on average, the improved PSO-based plan reduces the maximum dose to heart, spinal cord and esophagus by 43%, as compared to the conventional PSO, while swarm population is cut to half.

[1]  P. Keall.  4-dimensional computed tomography imaging and treatment planning. , 2004, Seminars in radiation oncology.

[2]  D K Smith,et al.  Numerical Optimization , 2001, J. Oper. Res. Soc..

[3]  H Shirato,et al.  Detection of lung tumor movement in real-time tumor-tracking radiotherapy. , 2001, International journal of radiation oncology, biology, physics.

[4]  Kamran Kiasaleh,et al.  Modification of Real-Number and Binary PSO Algorithms for Accelerated Convergence , 2011, IEEE Transactions on Antennas and Propagation.

[5]  Tony Wong,et al.  Dosimetric impact of breathing motion in lung stereotactic body radiotherapy treatment using intensity modulated radiotherapy and volumetric modulated arc therapy [corrected]. , 2012, International journal of radiation oncology, biology, physics.

[6]  Wufan Chen,et al.  A particle swarm optimization algorithm for beam angle selection in intensity-modulated radiotherapy planning , 2005, Physics in medicine and biology.

[7]  Nadia Nedjah,et al.  Three Alternatives for Parallel GPU-Based Implementations of High Performance Particle Swarm Optimization , 2013, IWANN.

[8]  Tumor control probability and the utility of 4D vs 3D dose calculations for stereotactic body radiotherapy for lung cancer. , 2015, Medical dosimetry : official journal of the American Association of Medical Dosimetrists.

[9]  Joao Seco,et al.  Motion management with phase-adapted 4D-optimization , 2010, Physics in medicine and biology.

[10]  Shaik Affijulla,et al.  Swarm intelligence solution to large scale thermal power plant Load Dispatch , 2011, 2011 International Conference on Emerging Trends in Electrical and Computer Technology.

[11]  George T. Y. Chen,et al.  Four-dimensional image-based treatment planning: Target volume segmentation and dose calculation in the presence of respiratory motion. , 2005, International journal of radiation oncology, biology, physics.

[12]  Xun Jia,et al.  A DVH-guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning. , 2014, Medical physics.

[13]  Michael B Sharpe,et al.  Significant reductions in heart and lung doses using deep inspiration breath hold with active breathing control and intensity-modulated radiation therapy for patients treated with locoregional breast irradiation. , 2003, International journal of radiation oncology, biology, physics.

[14]  Yoichi Watanabe,et al.  A Practical Guide to Intensity‐Modulated Radiation Therapy , 2003 .