ESTRO 2023 - Abstract Book

S1751

Digital Posters

ESTRO 2023

Conclusion TPS beam model calculate dose distributions of PBS beams using apertures with good level of accuracy. This finding needs to be confirmed for patient specific apertures with irregular shape. Absorbed dose measurement is challenging for beam of 1 cm or less. Deeper investigation and test with other small field detectors suitable for proton PBS beams would be recommended to evaluate correction factors that should be used with such small fields. Apertures in proton beams will increase neutron generation but is expected to be significantly lower than in passive systems, as in PBS protons interact only with block edges. Nevertheless, there is a decrease in lateral penumbra when using apertures, that could mean important dosimetry advantages in some clinical cases. S. Wuyckens 1 , G. Janssens 2 , L. Zhao 3 , X. Ding 3 , M. Chocan Vera 1 , H. Ozan 1 , A. Barragan Montero 1 , E. Sterpin 1,4 , J. Lee 1 , K. Souris 5,1 1 UCLouvain, MIRO, Brussels, Belgium; 2 Ion Beam Applications SA, R&D, Louvain-La-Neuve, Belgium; 3 Beaumont Health, Department of Radiation Oncology, Royal Oak, USA; 4 KULeuven, Department of Oncology, Leuven, Belgium; 5 Ion Beam Applications SA, IBA Dosimetry, Louvain-La-Neuve, Belgium Purpose or Objective In 2016, spot-scanning arc proton therapy (SPArc) algorithm [1] was introduced to solve the energy sequencing problem raised by the proton arc modality. The large number of irradiation angles translates indeed into a long beam delivery time if conventional plan optimization is performed. To stay within a reasonable timing, SPArc starts from a plan with very few irradiation directions and then iteratively splits the beams until a desired arc sampling frequency is reached. A filtering operation is then performed to restrict the number of energy layers to one in each angular sector. SPArc produced clinical arc plans competing with the conventional treatments in different disease sites. However, one of the obstacles to implementing SPArc in the clinic is its computing efficiency (time and memory usage). The main reason is that at each splitting operation, the beamlet matrix (influence map) has to be recomputed for all the spots due to the restructuring of the plan. To address this problem, we propose another method that avoids computing huge beamlet matrices, namely, the beamlet-free optimizer, an in-house Monte Carlo dose computation. Materials and Methods The two approaches (SPArc beamlet vs SPArc beamlet-free) are compared and evaluated on three patients: a brain, a lung, and a liver tumor case. The optimizations run in an in-house treatment planning system, OpenTPS. The beamlet-based method uses a commercial linear programming solver through the Python API while the beamlet-free is directly coded in the Monte Carlo dose engine, MCsquare [2], where the successive proton batches are used both to accumulate the dose and to adjust the spot weights in a stochastic gradient descent approach to optimize the plan objectives. PO-1982 Comparison of SPArc algorithm efficiency using beamlet-based and beamlet-free optimization schemes

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