pytrip.models.proton module

Collection of proton RBE models.

[1] A. Carabe, M. Moteabbed, N. Depauw, J. Schuemann, and H. Paganetti, “Range uncertainty in proton therapy due to variable biological effectiveness,” Phys. Med. Biol. 57(5), 1159-1172 (2012). https://doi.org/10.1088/0031-9155/57/5/1159

[2] M. Wedenberg, B. Lind, and B. Haardemark, “A model for the relative biological effectiveness of protons: The tissue specific parameter alpha/beta of photons is a predictor for the sensitivity to LET changes,” Acta Oncol. 52(3), 580-588 (2013). http://dx.doi.org/10.3109/0284186X.2012.705892

[3] A. L. McNamara, J. Schuemann, and H. Paganetti, “A phenomenological relative biological effectiveness (RBE) model for proton therapy based on all published in vitro cell survival data,” Phys. Med. Biol. 60(21), 8399-8416 https://doi.org/10.1088/0031-9155/60/21/8399

pytrip.models.proton.rbe_carabe(dose, let, abx)

Carabe proton RBE model

input parameters may be either numpy.array or scalars TODO: handle Cube() class directly

Params dose:physical proton dose in [Gy] Params let:LET in [keV/um] Params abx:alpha_x / beta_x [Gy^-1] Returns:RBE for the given parameters Ref:https://doi.org/10.1088/0031-9155/57/5/1159

pytrip.models.proton.rbe_mcnamara(dose, let, abx)

McNamara proton RBE model

input parameters may be either numpy.array or scalars TODO: handle Cube() class directly

Params dose:physical proton dose in [Gy] Params let:LET in [keV/um] Params abx:alpha_x / beta_x [Gy^-1] Returns:RBE for the given parameters Ref:https://doi.org/10.1088/0031-9155/60/21/8399

pytrip.models.proton.rbe_wedenberg(dose, let, abx)

Wedenberg proton RBE model

input parameters may be either numpy.array or scalars TODO: handle Cube() class directly

Params dose:physical proton dose in [Gy] Params let:LET in [keV/um] Params abx:alpha_x / beta_x [Gy^-1] Returns:RBE for the given parameters Ref:http://dx.doi.org/10.3109/0284186X.2012.705892