An optimization algorithm that incorporates IMRT delivery constraints.

An intensity-modulated beam optimization algorithm is presented which incorporates the delivery constraints into the optimization cycle. The optimization algorithm is based on the quasi-Newton method of iteratively solving minimization problems. The developed algorithm iteratively corrects the incident, pencil-beam-like, fluence to incorporate the delivery constraints. In the present study, the goal of the optimization algorithm is to achieve the best deliverable radiotherapy plan, subject to the constraints of the delivery technique described by a leaf-sequencing algorithm being applied concurrently. In general, if they are applied after, rather than during, the optimization cycle, the delivery constraints associated with the IMRT technique can produce local variations up to 6% in the 'optimized' dose (i.e., distribution without applied constraints) and reduce the degree of conformity, of the dose, to the PTV region. The optimization method has been applied to three IMRT delivery techniques: dynamic multileaf (DMLC), multiple-static-field (MSF) and slice-by-slice tomotherapy (NOMOS MIMiC). The beam profiles were generated for a prostate tumour with organs at risk being the rectum, bladder and femoral heads. The optimization method described was shown to generate optimum and deliverable IMRT plans for these three delivery techniques. In the case of the DMLC and MSF the optimization converged within 3-5 iterations to a mean PTV dose of 69.60 +/- 1.34 Gy and 69.71 +/- 1.34 Gy, respectively, while for NOMOS MIMiC approximately 10 iterations were needed to obtain 69.68 +/- 1.55 Gy. In addition to this, the IMRT optimization also yielded optimum fluence profiles when clustering was performed concurrently with the leaf-sequencer. An optimum between 8 and 15 clusters of equal fluence 'intensity' was shown to establish the best compromise between the number of fluence levels and the PTV dose coverage.