title: Automated fluence map optimization based on fuzzy inference systems.
abstract: OBJECTIVE
  The planning of an intensity modulated radiation therapy treatment requires the optimization of the fluence intensities. The fluence map optimization (FMO) is many times based on a nonlinear continuous programming problem, being necessary for the planner to define a priori weights and/or lower bounds that are iteratively changed within a trial-and-error procedure until an acceptable plan is reached. In this work, the authors describe an alternative approach for FMO that releases the human planner from trial-and-error procedures, contributing for the automation of the planning process.
  
  
  METHODS
  The FMO is represented by a voxel-based convex penalty continuous nonlinear model. This model makes use of both weights and lower/upper bounds to guide the optimization process toward interesting solutions that are able to satisfy all the constraints defined for the treatment. All the model's parameters are iteratively changed by resorting to a fuzzy inference system. This system analyzes how far the current solution is from a desirable solution, changing in a completely automated way both weights and lower/upper bounds. The fuzzy inference system is based on fuzzy reasoning that enables the use of common-sense rules within an iterative optimization process. The method is built in two stages: in a first stage, an admissible solution is calculated, trying to guarantee that all the treatment planning constraints are being satisfied. In this first stage, the algorithm tries to improve as much as possible the irradiation of the planning target volumes. In a second stage, the algorithm tries to improve organ sparing, without jeopardizing tumor coverage.
  
  
  RESULTS
  The proposed methodology was applied to ten head-and-neck cancer cases already treated in the Portuguese Oncology Institute of Coimbra (IPOCFG) and signalized as complex cases. IMRT treatment was considered, with 7, 9, and 11 equidistant beam angles. It was possible to obtain admissible solutions for all the patients considered and with no human planner intervention. The results obtained were compared with the optimized solution using a similar optimization model but with human planner intervention. For the vast majority of cases, it was possible to improve organ sparing and at the same time to assure better tumor coverage.
  
  
  CONCLUSIONS
  Embedding a fuzzy inference system into FMO allows human planner reasoning to be used in the guidance of the optimization process toward interesting regions in a truly automated way. The proposed methodology is capable of calculating high quality plans within reasonable computational times and can be an important contribution toward fully automated radiation therapy treatment planning.
authors.affiliation: FEUC and Inesc-Coimbra, University of Coimbra, Coimbra 3004512, Portugal.
authors.forename: Joana
authors.initials: J
authors.lastname: Dias
authors.affiliation: Inesc-Coimbra, University of Coimbra, Coimbra 3000033, Portugal.
authors.forename: Humberto
authors.initials: H
authors.lastname: Rocha
authors.affiliation: Medical Physics Department, IPOCFG, EPE, Coimbra, Coimbra 3000075, Portugal and Physics Department, University of Aveiro, Aveiro 3810193, Portugal.
authors.forename: Tiago
authors.initials: T
authors.lastname: Ventura
authors.affiliation: School of Allied Health Technologies Polytechnic Institute of Porto, Vila Nova de Gaia, 4400330 and I3N Department of Physics, Aveiro University, Aveiro 3810193 Portugal.
authors.forename: Brígida
authors.initials: B
authors.lastname: Ferreira
authors.affiliation: Medical Physics Department, IPOCFG, EPE, Coimbra 3000075, Portugal.
authors.forename: Maria do Carmo
authors.initials: Mdo C
authors.lastname: Lopes
mesh_terms: D001331:Automation; D017143:Fuzzy Logic; D006801:Humans; D058958:Organs at Risk / Q000528:radiation effects; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed