A DVH-guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning

Masoud Zarepisheh, Troy Long, Nan Li, Zhen Tian, H. Edwin Romeijn, Xun Jia, Steve B. Jiang

Research output: Contribution to journalArticle

60 Scopus citations

Abstract

Purpose: To develop a novel algorithm that incorporates prior treatment knowledge into intensity modulated radiation therapy optimization to facilitate automatic treatment planning and adaptive radiotherapy (ART) replanning. Methods: The algorithm automatically creates a treatment plan guided by the DVH curves of a reference plan that contains information on the clinician-approved dose-volume trade-offs among different targets/organs and among different portions of a DVH curve for an organ. In ART, the reference plan is the initial plan for the same patient, while for automatic treatment planning the reference plan is selected from a library of clinically approved and delivered plans of previously treated patients with similar medical conditions and geometry. The proposed algorithm employs a voxel-based optimization model and navigates the large voxel-based Pareto surface. The voxel weights are iteratively adjusted to approach a plan that is similar to the reference plan in terms of the DVHs. If the reference plan is feasible but not Pareto optimal, the algorithm generates a Pareto optimal plan with the DVHs better than the reference ones. If the reference plan is too restricting for the new geometry, the algorithm generates a Pareto plan with DVHs close to the reference ones. In both cases, the new plans have similar DVH trade-offs as the reference plans. Results: The algorithm was tested using three patient cases and found to be able to automatically adjust the voxel-weighting factors in order to generate a Pareto plan with similar DVH trade-offs as the reference plan. The algorithm has also been implemented on a GPU for high efficiency. Conclusions: A novel prior-knowledge-based optimization algorithm has been developed that automatically adjust the voxel weights and generate a clinical optimal plan at high efficiency. It is found that the new algorithm can significantly improve the plan quality and planning efficiency in ART replanning and automatic treatment planning.

Original languageEnglish (US)
Article number061711
JournalMedical physics
Volume41
Issue number6
DOIs
StatePublished - Jun 2014

Keywords

  • Pareto surface
  • adaptive radiotherapy re-planning
  • automatic treatment planning
  • intensity modulation
  • optimization

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

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