title: Beam's-eye-view Dosimetrics-guided inverse planning for aperture-modulated arc therapy.
abstract: OBJECTIVE
  To use angular beam's-eye-view dosimetrics (BEVD) information to improve the computational efficiency and plan quality of inverse planning of aperture-modulated arc therapy (AMAT).
  
  
  METHODS
  In BEVD-guided inverse planning, the angular space spanned by a rotational arc is represented by a large number of fixed-gantry beams with angular spacing of approximately 2.5 degrees. Each beam is assigned with an initial aperture shape determined by the beam's-eye-view (BEV) projection of the planning target volume (PTV) and an initial weight. Instead of setting the beam weights arbitrarily, which slows down the subsequent optimization process and may result in a suboptimal solution, a priori knowledge about the quality of the beam directions derived from a BEVD is adopted to initialize the weights. In the BEVD calculation, a higher score is assigned to directions that allow more dose to be delivered to the PTV without exceeding the dose tolerances of the organs at risk (OARs) and vice versa. Simulated annealing is then used to optimize the segment shapes and weights. The BEVD-guided inverse planning is demonstrated by using two clinical cases, and the results are compared with those of a conventional approach without BEVD guidance.
  
  
  RESULTS
  An a priori knowledge-guided inverse planning scheme for AMAT is established. The inclusion of BEVD guidance significantly improves the convergence behavior of AMAT inverse planning and results in much better OAR sparing as compared with the conventional approach.
  
  
  CONCLUSIONS
  BEVD-guidance facilitates AMAT treatment planning and provides a comprehensive tool to maximally use the technical capacity of the new arc therapeutic modality.
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA 94305-5847, USA.
authors.forename: Yunzhi
authors.initials: Y
authors.lastname: Ma
authors.forename: Richard
authors.initials: R
authors.lastname: Popple
authors.forename: Tae-Suk
authors.initials: TS
authors.lastname: Suh
authors.forename: Lei
authors.initials: L
authors.lastname: Xing
mesh_terms: D000465:Algorithms; D006801:Humans; D008297:Male; D020714:Maximum Tolerated Dose; D010190:Pancreatic Neoplasms / Q000532:radiotherapy; D010825:Physics; D011471:Prostatic Neoplasms / Q000532:radiotherapy; D011836:Radiation Tolerance; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods* / Q000592:standards; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods* / Q000592:standards; D013679:Technology, Radiologic / Q000379:methods