intensity-modulated radiotherapy: current status and issues

IMRT: current status and issues of interest ● IMRT CWG
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Fig. 9. Set of central axis 4 MV X-ray depth–dose curves computed for a variety of field sizes in a water phantom with
a lung-equivalent slab (0.3 g/cm 3 ). The 1 cm 1cmfield size is typical of the pencil beam size used in IMRT.
ual beamlet is calculated using the product of the inverse
square law, the Tissue-Maximum Ratio (TMR) for the pencil
beam, an off-axis ratio, the appropriate output factor, the
MUs delivered, and various constants or other correction
factors. Models with this kind of simplicity offer significant
speed advantages for use in the optimization code but have
varying limitations in accuracy.
A third kind of calculation algorithm is based on the
kernel-based models, which directly compute the dose in a
phantom or patient. Typical of this type of model are
convolution/superposition algorithms that can take into account
beam energy, geometry, beam modifiers, patient contour,
and electron density distribution (141–145), although
there are also pencil beam approaches (146) that bridge the
gap between the empirically based correction models (like
the finite-size pencil beam) and true kernel-based convolution
models. Unlike correction-based methods, kernel-based
approaches do not first compute the dose in a water phantom
and correct the water dose for the treatment circumstances.
Both the convolution method and the Monte Carlo method
(discussed below) compute the dose per unit energy fluence
(or fluence) incident on the patient.
Convolution methods are capable of accounting for electronic
disequilibrium effects (141–145) and may more accurately
deal with inhomogeneities and other complex aspects
of IMRT dose calculations than the models described
above. Fig. 9 illustrates the potential for large differences in
the dose due to inhomogeneities for small beamlets. In this
example (water phantom with a lung-equivalent slab), the
depth-dose for the 10 cm 10 cm field is only slightly
perturbed because of the reduced attenuation of the primary
photons. However, the depth-dose for the 5 cm 5cmfield
has a reduced dose because of the streaming of electrons
laterally, and the 1 cm 1cmfield, typical of the area of
an IMRT beamlet, shows severe perturbation because of
electronic disequilibrium. The perturbing effect of electronic
disequilibrium increases with the beam energy (147,
148). It is evident from this simple example that the development
of IMRT plans requires a dose-calculation method
that can accurately predict electronic disequilibrium effects.
Although it is clear that the improved dose-calculation
accuracy afforded by the convolution-type calculations may
be important for IMRT, the long calculation times make this
difficult. Currently, many optimization approaches do not
use these more accurate calculations, or only use them at the
end of the optimization to calculate the final delivered dose
distribution. Only one group has reported the use of full
convolution/superposition calculations within the optimization
iteration loop (149).
Recently, significant progress has been made in the development
of Monte Carlo calculation algorithms for photon
beams that are fast enough to compete with other current
methods (150–153). In several situations, the Monte Carlo
method is likely to be even more accurate than the convolution
method (154). For example, multiple scatter (second
and higher order scatter) may be perturbed near the surface
of a patient and the Monte Carlo method may be able to
account for this as long as the number of simulated particles
(called histories) is sufficient to compute the multiple scattered
photon dose accurately. In addition, the “density scaling”
method (154) used by the superposition/convolution
methods to correct for heterogeneities may not be adequate
for primary electrons set in motion, especially in higher
atomic number materials, such as bone or metal prostheses,