ESTRO 2023 - Abstract Book

S1761

Digital Posters

ESTRO 2023

this point was only picked to demonstrate the potential of BCO for PAT, the task of finding the best compromise remains a clinical decision.

ROI

Metric Limit (Gy) D_t

d_t d_T

Dosimetry

Target

D98 51.30

53.28 53.06 53.28 56.73 57.45 61.58 54.14 54.87 58.34 4.34 4.00 3.45 17.37 16.54 14.47 54.04 55.53 54.87

D5

56.70 54.25

Optical chiasm D5

Brain stem

Dmean 21.50

D5 D5

54.00 54.50

LON

Irradiation time (s) 126.20 69.40 24.1 Table 1: Summary of optimization results for the brain case for three points standing on Pareto front

Conclusion This study introduces the application of BCO to the PAT problem, which eventually permits the planners to select the best treatment strategy according to their own preferences.

PO-1989 Develop a fast spot sparsity optimization algorithm for proton arc therapy

X. Ding 1 , L. Zhao 1 , J. You 2 , G. Liu 3 , S. Wuyckens 4 , X. Lu 5

1 Corewell Health William Beaumont University hospital, Radiation Oncology, Royal Oak, USA; 2 , The Hong Kong University of Science and Technology, Department of Mathematics, Hongkong, China; 3 Huazhong University of Science and Technology, 3Cancer Center, Union Hospital, Tongji Medical College, Wuhan, China; 4 UCLouvain, 4Molecular Imaging, Radiotherapy and Oncology (MIRO), Louvain-la-Neuve, Belgium; 5 Wuhan University, School of Mathematics and Statistics, Wuhan, China Purpose or Objective We developed a primal dual active set with continuation (PDASC) for a fast spot sparsity optimization algorithm by reducing the computation time compared to the previous framework on alternating direction method of multipliers (ADMM) for spot- scanning proton arc (SPArc) therapy. Materials and Methods Based on machine-specific beam delivery sequence model of IBA’s ProtetusONE® proton system in Beaumont Proton Therapy Center, beam delivery time is approximately proportional to spot number. Then we propose a l0 -norm sparsity optimization formulation to reduce the spot number. Sparsity level can be controlled by adjusting l0 -norm coefficient. PDASC was developed to search for the spot sparsity solution in this non-convex optimization problem. Our previous work