IMRT Treatment Planning on 4D Geometries for the Era of Dynamic MLC Tracking

The problem addressed here was to obtain optimal and deliverable dynamic multileaf collimator (MLC) leaf sequences from four-dimensional (4D) geometries for dynamic MLC tracking delivery. The envisaged scenario was where respiratory phase and position information of the target was available during treatment, from which the optimal treatment plan could be further adapted in real time. A tool for 4D treatment plan optimization was developed that integrates a commercially available treatment planning system and a general-purpose optimization system. The 4D planning method was applied to the 4D computed tomography planning scans of three lung cancer patients. The optimization variables were MLC leaf positions as a function of monitor units and respiratory phase. The objective function was the deformable dose-summed 4D treatment plan score. MLC leaf motion was constrained by the maximum leaf velocity between control points in terms of monitor units for tumor motion parallel to the leaf travel direction and between phases for tumor motion parallel to the leaf travel direction. For comparison and a starting point for the 4D optimization, three-dimensional (3D) optimization was performed on each of the phases. The output of the 4D IMRT planning process is a leaf sequence which is a function of both monitor unit and phase, which can be delivered to a patient whose breathing may vary between the imaging and treatment sessions. The 4D treatment plan score improved during 4D optimization by 34%, 4%, and 50% for Patients A, B, and C, respectively, indicating 4D optimization generated a better 4D treatment plan than the deformable sum of individually optimized phase plans. The dose-volume histograms for each phase remained similar, indicating robustness of the 4D treatment plan to respiratory variations expected during treatment delivery. In summary, 4D optimization for respiratory phase-dependent treatment planning with dynamic MLC motion tracking improved the 4D treatment plan score by 4–50% compared with 3D optimization. The 4D treatment plans had leaf sequences that varied from phase to phase to account for anatomic motion, but showed similar target dose distributions in each phase. The current method could in principle be generalized for use in offline replanning between fractions or for online 4D treatment planning based on 4D cone-beam CT images. Computation time remains a challenge.