Purpose: Current radiation therapy (RT) is a static process, where beam intensities are calculated before the start of treatment, thus anatomical position changes over time can result in poor dose conformity. To overcome these limitations, we present a simulation study on a fully dynamic real-time adaptive radiation therapy (RT-ART) optimization approach that uses ultra-fast beamlet control to dynamically adapt to patient motion in real-time.
Methods: A virtual RT-ART machine was simulated with a rapidly rotating linear accelerator source (60 RPM) and a binary 1D multi-leaf collimator at 100Hz. If the real-time tracked target motion exceeded a predefined threshold, a time dependent objective function was solved using fast optimization methods to calculate new beamlet intensities that were then delivered to the patient.
Results: To evaluate the approach, system response was analyzed for patient derived continuous drift, step-like, and periodic intra-fractional motion. For each motion type investigated, the RT-ART method was compared against the ideal case with no patient motion (static case) as well as to the case without the use RT-ART. In all cases, isodose lines and dose-volume-histograms showed that RT-ART plan quality was approximately the same as the static case, and considerably better than the no RT-ART case. On average, the RT-ART method resulted in a PTV-D95 of 97.2%, which compares well with 96.8% for the static plan with no motion. Without, the use of RT-ART, the PTV-D95 decreased to 75.6%.
Conclusion: The RT-ART optimization framework has the potential to optimally deliver dose to a patient in a way that fully accounts for anatomical changes due to motion. With continued advances in real-time patient motion tracking and fast computational processes, there is significant potential for the RT-ART optimization process to be realized on next generation RT machines.
Funding Support, Disclosures, and Conflict of Interest: Funding was provided in part by NIH grant R01CA227124.