A variable fluence step clustering and segmentation algorithm for step and shoot IMRT

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Abstract
A step and shoot sequencer was developed that can be integrated into an IMRT optimization algorithm. The method uses non-uniform fluence steps and is adopted to the constraints of an MLC. It consists of a clustering, a smoothing and a segmentation routine. The performance of the algorithm is demonstrated for eight mathematical profiles of differing complexity and two optimized profiles of a clinical prostate case. The results in terms of stability, flexibility, speed and conformity fulfil the criteria for the integration into the optimization concept. The performance of the clustering routine is compared with another previously published one (Bortfeld et al 1994 Int. J. Radiat. Oncol. Biol. Phys. 28 723-30) and yields slightly better results in terms of mean and maximum deviation between the optimized and the clustered profile. We discuss the specific attributes of the algorithm concerning its integration into the optimization concept.