title: Dose domain regularization of MLC leaf patterns for highly complex IMRT plans.
abstract: OBJECTIVE
  The advent of automated beam orientation and fluence optimization enables more complex intensity modulated radiation therapy (IMRT) planning using an increasing number of fields to exploit the expanded solution space. This has created a challenge in converting complex fluences to robust multileaf collimator (MLC) segments for delivery. A novel method to regularize the fluence map and simplify MLC segments is introduced to maximize delivery efficiency, accuracy, and plan quality.
  
  
  METHODS
  In this work, we implemented a novel approach to regularize optimized fluences in the dose domain. The treatment planning problem was formulated in an optimization framework to minimize the segmentation-induced dose distribution degradation subject to a total variation regularization to encourage piecewise smoothness in fluence maps. The optimization problem was solved using a first-order primal-dual algorithm known as the Chambolle-Pock algorithm. Plans for 2 GBM, 2 head and neck, and 2 lung patients were created using 20 automatically selected and optimized noncoplanar beams. The fluence was first regularized using Chambolle-Pock and then stratified into equal steps, and the MLC segments were calculated using a previously described level reducing method. Isolated apertures with sizes smaller than preset thresholds of 1-3 bixels, which are square units of an IMRT fluence map from MLC discretization, were removed from the MLC segments. Performance of the dose domain regularized (DDR) fluences was compared to direct stratification and direct MLC segmentation (DMS) of the fluences using level reduction without dose domain fluence regularization.
  
  
  RESULTS
  For all six cases, the DDR method increased the average planning target volume dose homogeneity (D95/D5) from 0.814 to 0.878 while maintaining equivalent dose to organs at risk (OARs). Regularized fluences were more robust to MLC sequencing, particularly to the stratification and small aperture removal. The maximum and mean aperture sizes using the DDR were consistently larger than those from DMS for all tested number of segments.
  
  
  CONCLUSIONS
  The fluence map to MLC segmentation conversion problem was formulated as a secondary optimization problem in the dose domain to minimize the smoothness-regularized dose discrepancy. The large scale optimization problem was solved using a primal-dual algorithm that transformed complicated fluences into maps that were more robust to the MLC segmentation and sequencing, affording fewer and larger segments with minimal degradation to dose distribution.
authors.affiliation: Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California 90095.
authors.forename: Dan
authors.initials: D
authors.lastname: Nguyen
authors.affiliation: Department of Mathematics, University of California Los Angeles, Los Angeles, California 90095.
authors.forename: Daniel
authors.initials: D
authors.lastname: O'Connor
authors.affiliation: Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California 90095.
authors.forename: Victoria Y
authors.initials: VY
authors.lastname: Yu
authors.affiliation: Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California 90095.
authors.forename: Dan
authors.initials: D
authors.lastname: Ruan
authors.affiliation: Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California 90095.
authors.forename: Minsong
authors.initials: M
authors.lastname: Cao
authors.affiliation: Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California 90095.
authors.forename: Daniel A
authors.initials: DA
authors.lastname: Low
authors.affiliation: Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California 90095.
authors.forename: Ke
authors.initials: K
authors.lastname: Sheng
mesh_terms: D000465:Algorithms; D001921:Brain / Q000528:radiation effects; D005720:Gamma Rays / Q000627:therapeutic use; D005909:Glioblastoma / Q000532:radiotherapy; D006258:Head and Neck Neoplasms / Q000532:radiotherapy; D008175:Lung Neoplasms / Q000532:radiotherapy; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods; D050397:Radiotherapy, Intensity-Modulated / Q000295:instrumentation* / Q000379:methods*; D013997:Time Factors