A method to dynamically balance intensity modulated radiotherapy dose between organs-at-risk.
The IMRT treatment planning process typically follows a path that is based on the manner in which the planner interactively adjusts the target and organ-at-risk (OAR) constraints and priorities. The time-intensive nature of this process restricts the planner from fully understanding the dose tradeoff between structures, making it unlikely that the resulting plan fully exploits the extent to which dose can be redistributed between anatomical structures. Multiobjective Pareto optimization has been used in the past to enable the planner to more thoroughly explore alternatives in dose trade-off by combining pre-generated Pareto optimal solutions in real time, thereby potentially tailoring a plan more exactly to requirements. However, generating the Pareto optimal solutions can be nonintuitive and computationally time intensive. The author presents an intuitive and fast non-Pareto approach for generating optimization sequences (prior to planning), which can then be rapidly combined by the planner in real time to yield a satisfactory plan. Each optimization sequence incrementally reduces dose to one OAR at a time, starting from the optimization solution where dose to all OARs are reduced with equal priority, until user-specified target coverage limits are violated. The sequences are computationally efficient to generate, since the optimization at each position along a sequence is initiated from the end result of the previous position in the sequence. The pre-generated optimization sequences require no user interaction. In real time, a planner can more or less instantaneously visualize a treatment plan by combining the dose distributions corresponding to user-selected positions along each of the optimization sequences (target coverage is intrinsically maintained in the combination). Interactively varying the selected positions along each of the sequences enables the planner to rapidly understand the nature of dose trade-off between structures and, thereby, arrive at a suitable plan in a short time. This methodology is demonstrated on a prostate cancer case and olfactory neuroblastoma case.
Das
Shiva K
SK
Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710, USA. shiva.das@duke.edu
D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D011832:Radiation Injuries / Q000209:etiology* / Q000517:prevention & control*; D011874:Radiometry / Q000379:methods*; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D020266:Radiotherapy, Conformal / Q000009:adverse effects* / Q000379:methods*; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D014781:Viscera / Q000528:radiation effects