Program Information

A Novel IMRT Plan Optimization Algorithm for Physician-Driven Plan Tuning

M Zarepisheh*, N Li, L Cervino, K Moore, X Jia, S Jiang, Center for Advanced Radiotherapy Technologies, University of California, San Diego, La Jolla, CA

SU-E-T-588 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

Purpose:
To greatly speed up the time-consuming treatment planning procedure by developing a new optimization algorithm allowing the physicians to interactively fine-tune the DVHs and iso-dose lines of an IMRT plan.

Methods:
The conventional treatment planning procedure is a time-consuming and resource-demanding task that may need multiple iterations between the dosimetrists and clinicians after an initial plan is developed. We develop a new optimization algorithm to speed up this procedure by allowing the physician to interactively fine-tune the DVH and iso-dose lines on top of the initially optimized plan. After the physician modifies the DVH and iso-dose lines of the current plan towards a more desired one through an interactive graphical interface, the algorithm will adjust the voxel-dependent optimization parameters to guide the plan towards the modified one. To ease the fine-tuning procedure, the algorithm enables the physician to lock some DVH curves as well as to change the priorities of the organs. The algorithm explores a large Pareto surface by adjusting voxel-dependent parameters to find out a plan that is the closest to the physicians desired plan.

Results:
The algorithm was tested using a series of clinically realistic patient data and found to have desirable performance. It can adjust the voxel-dependent parameters and guide a plan towards the physician-driven fine-tuned plan. This algorithm has been implemented on GPU for high efficiency, and the updating procedure is near real time.

Conclusion:
The conventional treatment planning procedure can be significantly improved in terms of efficiency and physician satisfactory by utilizing the new GPU-based algorithm allowing the physician to interactively fine-tune the plan in near real time.

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