Development of a GPU Research Platform for Automatic Treatment Planning and Adaptive Radiotherapy Re-Planning
Q Gautier*, Z Tian, Y Graves, N Li, M Zarepisheh, C Sutterley, F Shi, L Cervino, X Jia, S Jiang, Center for Advanced Radiotherapy Technologies, University of California, San Diego, La Jolla, CATH-C-137-10 Thursday 10:30AM - 12:30PM Room: 137
Purpose: To develop a research platform called SCORE (Super Computing Online Re-planning Environment) for automatic treatment planning and adaptive radiotherapy re-planning based on GPU.
Methods: Our software is a Graphical User Interface (GUI) based on the Qt framework that allows users to easily and quickly create a new treatment plan based on a reference plan. It consists of several modules, including loading plan and patient geometry from DICOM RT files, automatic and manual rigid registration, deformable registration for contour propagation, previous plan based automatic plan optimization, physician-driven plan tuning, final dose calculation, and plan exporting in DICOM RT format. For automatic planning, a reference plan is identified from a library of previously delivered plans and it is used to guide the optimization process. For adaptive radiotherapy re-planning, the original plan of the same patient is used as the reference plan to guide the optimization process to generate a new plan on the new patient geometry defined by either a new CT or Cone-Beam CT image. All the computation modules have been implemented in CUDA to achieve high efficiency. The results for each step of the workflow can be visualized for review, revision, and approval.
Results: SCORE has been well tested and validated for prostate and head/neck cancer cases. The validation was done by comparing SCORE plans against the same plans with re-calculated dose distributions and DVHs using a commercial planning system. We found that SCORE can generate clinically optimal treatment plans that are realistic and deliverable. The plans can be automatically created in only a few minutes, followed by another few minutes of physician fine-tuning using an interactive GUI.
Conclusion: We have developed a very efficient and user-friendly GPU-based research platform that can be used for clinical research on automatic treatment planning and adaptive radiotherapy re-planning.
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