  In this paper, the tools provided by the theory of Optimal Experimental
Design are applied to a nonlinear calibration model. This is motivated by the
need of estimating radiation doses using radiochromic films for radiotherapy
purposes. The calibration model is in this case nonlinear and the explanatory
variable cannot be worked out explicitly from the model. In this case an
experimental design has to be found on the dependent variable. For that, the
inverse function theorem will be used to obtain an information matrix to be
optimized. Optimal designs on the response variable are computed from two
different perspectives, first for fitting the model and estimating each of the
parameters and then for predicting the proper dose to be applied to the
patient. While the first is a common point of view in a general context of the
Optimal Experimental Design, the latter is actually the main objective of the
calibration problem for the practitioners and algorithms for computing these
optimal designs are also provided.
