Using voxel-dependent importance factors for interactive DVH-based dose optimization.
Intensity modulated radiation therapy (IMRT) inverse planning is usually performed by pre-selecting parameters such as beam modality, beam configuration and importance factors and then optimizing the fluence profiles or beamlet weights. In reality, the IMRT dose optimization problem may be ill-conditioned and there may not be a physical solution to account for the chosen parameters and constraints. Planner intervention is often required to conduct a multiple trial-and-error process where several parameters are sequentially varied until an acceptable compromise is achieved. The resulting solution reflects a balance between the conflicting requirements of the target and the sensitive structures. A major problem of the conventional inverse planning formalism is that there exists no effective mechanism for a planner to fine-tune the dose distribution on a local level or to differentially modify the dose-volume histograms (DVHs) of the involved structures. In this paper we introduce a new inverse planning scheme with voxel-dependent importance factors and demonstrate that it provides us with an effective link between the system parameters and the dosimetric behaviour at a local level. The planning proceeds in two steps. After a conventional trial-and-error inverse planning procedure is completed, we identify the dose interval at which the fractional volume on the DVH curve needs to be changed. The voxels that receive dose in the selected range are then located and their voxel-dependent importance factors are adjusted accordingly. The fine-tuning of the DVHs is iterative in nature and, using widely available computer graphic software tools, the process can be made graphically interactive. The new IMRT planning scheme is applied to two test cases and the results indicate that our control over the differential shapes of the DVHs of the involved structures is,greatly enhanced. Thus the technique may have significant practical implications in facilitating the IMRT treatment planning process.
Cotrutz
Cristian
C
Department of Radiation Oncology, Stanford University School of Medicine, CA 94305-5304, USA. cristian@reyes.stanford.edu
Xing
Lei
L
D000465:Algorithms; D001932:Brain Neoplasms / Q000532:radiotherapy; D004307:Dose-Response Relationship, Radiation; D006801:Humans; D015233:Models, Statistical; D009303:Nasopharyngeal Neoplasms / Q000532:radiotherapy; D009369:Neoplasms / Q000532:radiotherapy*; D019047:Phantoms, Imaging; D011874:Radiometry / Q000379:methods*; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods; D020266:Radiotherapy, Conformal / Q000379:methods*