Inverse planning for IMRT with nonuniform beam profiles using total-variation regularization (TVR)

Taeho Kim, Lei Zhu, Tae Suk Suh, Sarah Geneser, Bowen Meng, Lei Xing

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

Purpose: Radiation therapy with high dose rate and flattening filter-free (FFF) beams has the potential advantage of greatly reduced treatment time and out-of-field dose. Current inverse planning algorithms are, however, not customized for beams with nonuniform incident profiles and the resultant IMRT plans are often inefficient in delivery. The authors propose a total-variation regularization (TVR)-based formalism by taking the inherent shapes of incident beam profiles into account. Methods: A novel TVR-based inverse planning formalism is established for IMRT with nonuniform beam profiles. The authors introduce a TVR term into the objective function, which encourages piecewise constant fluence in the nonuniform FFF fluence domain. The proposed algorithm is applied to lung and prostate and head and neck cases and its performance is evaluated by comparing the resulting plans to those obtained using a conventional beamlet-based optimization (BBO). Results: For the prostate case, the authors' algorithm produces acceptable dose distributions with only 21 segments, while the conventional BBO requires 114 segments. For the lung case and the head and neck case, the proposed method generates similar coverage of target volume and sparing of the organs-at-risk as compared to BBO, but with a markedly reduced segment number. Conclusions: TVR-based optimization in nonflat beam domain provides an effective way to maximally leverage the technical capacity of radiation therapy with FFF fields. The technique can generate effective IMRT plans with improved dose delivery efficiency without significant deterioration of the dose distribution.

Original languageEnglish
Pages (from-to)57-66
Number of pages10
JournalMedical physics
Volume38
Issue number1
DOIs
StatePublished - Jan 2011

Keywords

  • IMRT
  • compressed sensing
  • flattening filter
  • inverse planning
  • total-variation

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