#
# Copyright (C) 2010-2017 PyTRiP98 Developers.
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# PyTRiP98 is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
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"""
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
"""
import logging
import numpy as np
logger = logging.getLogger(__name__)
[docs]def 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
"""
_labx = 2.686 * let / abx
_apx = 0.843 + 0.154 * _labx
_bpx = 1.090 + 0.006 * _labx
_bpx *= _bpx
rbe = _rbe_apx(dose, _apx, _bpx, abx)
return rbe
[docs]def 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
"""
_apx = 1.000 + 0.434 * let / abx
_bpx = 1.000
rbe = _rbe_apx(dose, _apx, _bpx, abx)
return rbe
[docs]def 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
"""
_apx = 0.999064 + 0.35605 * let / abx
_bpx = 1.1012 - 0.0038703 * np.sqrt(abx) * let
_bpx *= _bpx
rbe = _rbe_apx(dose, _apx, _bpx, abx)
return rbe
def _rbe_apx(dose, apx, bpx, abx):
"""
:params dose: proton dose [Gy]
:params apx: alpha_p / alpha_x [dimensionless] RBE_max = ap/ax when (dose -> 0 Gy)
:params bpx: beta_p / beta_x [dimensionless] RBE_min = sqrt(bp/bx) when (dose -> inf Gy)
:params abx: alpha_x / beta_x [Gy^-1]
"""
_rbe = (np.sqrt(abx*abx + 4*abx*apx*dose + 4*bpx * dose*dose) - abx) / (2 * dose)
return _rbe