AbstractID: 7004 Title: The application of a fuzzy inference system in inverse planning
Inverse planning of a complex geometry typically requires many trials to manually
adjust dose prescription and weight factors in order to compromise the dose distribution
between the target and critical organ. The process is time-consuming and the results
may not be optimal. In this study, a fuzzy inference system (FIS) was developed to deal
with non-unique dose prescription in inverse planning of complex geometry. In this FIS,
the expert knowledge is incorporated into the process of compromising dose
prescription for different structures. We defined the input-output relationship of a FIS
by a collection of fuzzy rules involving linguistic variable. FIS is then used to
automatically adjust dose prescription and weight factors in inverse planning.
The preliminary scheme of FIS consists of three parts: 1) fuzzification of input
prescription and weights by membership functions; 2) construction of rules which
express non-unique dose-volume relationship by means of membership functions; 3)
defuzzificaton of output by aggregating fuzzy sets to a unique prescription and weights.
The output of an optimization algorithm is input to the FIS and the output of FIS is used
as the input of the optimization algorithm. This procedure was repeated until the
outputs of optimization algorithm meet the convergence rules.
We applied the FIS (include 54 rules and 6 input and 3 output) to clinical examples.
Compared to the results obtained by manual trail, FIS is able to automatically achieve
dose-volume compromise between different structures. The FIS has the potential to
assist inverse treatment planning.