title: Fast leaf-fitting with generalized underdose/overdose constraints for real-time MLC tracking.
abstract: OBJECTIVE
  Real-time multileaf collimator (MLC) tracking is a promising approach to the management of intrafractional tumor motion during thoracic and abdominal radiotherapy. MLC tracking is typically performed in two steps: transforming a planned MLC aperture in response to patient motion and refitting the leaves to the newly generated aperture. One of the challenges of this approach is the inability to faithfully reproduce the desired motion-adapted aperture. This work presents an optimization-based framework with which to solve this leaf-fitting problem in real-time.
  
  
  METHODS
  This optimization framework is designed to facilitate the determination of leaf positions in real-time while accounting for the trade-off between coverage of the PTV and avoidance of organs at risk (OARs). Derived within this framework, an algorithm is presented that can account for general linear transformations of the planned MLC aperture, particularly 3D translations and in-plane rotations. This algorithm, together with algorithms presented in Sawant et al. ["Management of three-dimensional intrafraction motion through real-time DMLC tracking," Med. Phys. 35, 2050-2061 (2008)] and Ruan and Keall [Presented at the 2011 IEEE Power Engineering and Automation Conference (PEAM) (2011) (unpublished)], was applied to apertures derived from eight lung intensity modulated radiotherapy plans subjected to six-degree-of-freedom motion traces acquired from lung cancer patients using the kilovoltage intrafraction monitoring system developed at the University of Sydney. A quality-of-fit metric was defined, and each algorithm was evaluated in terms of quality-of-fit and computation time.
  
  
  RESULTS
  This algorithm is shown to perform leaf-fittings of apertures, each with 80 leaf pairs, in 0.226 ms on average as compared to 0.082 and 64.2 ms for the algorithms of Sawant et al., Ruan, and Keall, respectively. The algorithm shows approximately 12% improvement in quality-of-fit over the Sawant et al. approach, while performing comparably to Ruan and Keall.
  
  
  CONCLUSIONS
  This work improves upon the quality of the Sawant et al. approach, but does so without sacrificing run-time performance. In addition, using this framework allows for complex leaf-fitting strategies that can be used to account for PTV/OAR trade-off during real-time MLC tracking.
authors.affiliation: Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas 75390.
authors.forename: Douglas
authors.initials: D
authors.lastname: Moore
authors.affiliation: Department of Radiation Oncology, University of California, Los Angeles, California 90095.
authors.forename: Dan
authors.initials: D
authors.lastname: Ruan
authors.affiliation: Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas 75390.
authors.forename: Amit
authors.initials: A
authors.lastname: Sawant
mesh_terms: D000465:Algorithms; D019583:Dose Fractionation, Radiation; D006801:Humans; D008175:Lung Neoplasms / Q000503:physiopathology / Q000532:radiotherapy; D009068:Movement; D011829:Radiation Dosage; D011879:Radiotherapy Dosage; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods*; D013997:Time Factors