title: A method to reconstruct and apply 3D primary fluence for treatment delivery verification.
abstract: BACKGROUND
  In this study, a method is reported to perform IMRT and VMAT treatment delivery verification using 3D volumetric primary beam fluences reconstructed directly from planned beam parameters and treatment delivery records. The goals of this paper are to demonstrate that 1) 3D beam fluences can be reconstructed efficiently, 2) quality assurance (QA) based on the reconstructed 3D fluences is capable of detecting additional treatment delivery errors, particularly for VMAT plans, beyond those identifiable by other existing treatment delivery verification methods, and 3) QA results based on 3D fluence calculation (3DFC) are correlated with QA results based on physical phantom measurements and radiation dose recalculations.
  
  
  METHODS
  Using beam parameters extracted from DICOM plan files and treatment delivery log files, 3D volumetric primary fluences are reconstructed by forward-projecting the beam apertures, defined by the MLC leaf positions and modulated by beam MU values, at all gantry angles using first-order ray tracing. Treatment delivery verifications are performed by comparing 3D fluences reconstructed using beam parameters in delivery log files against those reconstructed from treatment plans. Passing rates are then determined using both voxel intensity differences and a 3D gamma analysis. QA sensitivity to various sources of errors is defined as the observed differences in passing rates. Correlations between passing rates obtained from QA derived from both 3D fluence calculations and physical measurements are investigated prospectively using 20 clinical treatment plans with artificially introduced machine delivery errors.
  
  
  RESULTS
  Studies with artificially introduced errors show that common treatment delivery problems including gantry angle errors, MU errors, jaw position errors, collimator rotation errors, and MLC leaf position errors were detectable at less than normal machine tolerances. The reported 3DFC QA method has greater sensitivity than measurement-based QA methods. Statistical analysis-based Spearman's correlations shows that the 3DFC QA passing rates are significantly correlated with passing rates of physical phantom measurement-based QA methods.
  
  
  CONCLUSIONS
  Among measurement-less treatment delivery verification methods, the reported 3DFC method is less demanding than those based on full dose re-calculations, and more comprehensive than those that solely checks beam parameters in treatment log files. With QA passing rates correlating to measurement-based passing rates, the 3DFC QA results could be useful for complementing the physical phantom measurements, or verifying treatment deliveries when physical measurements are not available. For the past 4+ years, the reported method has been implemented at authors' institution 1) as a complementary metric to physical phantom measurements for pretreatment, patient-specific QA of IMRT and VMAT plans, and 2) as an important part of the log file-based automated verification of daily patient treatment deliveries. It has been demonstrated to be useful in catching both treatment plan data transfer errors and treatment delivery problems.
authors.affiliation: Department of Radiation Oncology, School of Medicine, Washington University, St. Louis, MO, USA.
authors.forename: Shi
authors.initials: S
authors.lastname: Liu
authors.affiliation: Department of Radiation Oncology, School of Medicine, Washington University, St. Louis, MO, USA.
authors.forename: Thomas R
authors.initials: TR
authors.lastname: Mazur
authors.affiliation: Department of Radiation Oncology, School of Medicine, Washington University, St. Louis, MO, USA.
authors.forename: Harold
authors.initials: H
authors.lastname: Li
authors.affiliation: Department of Radiation Oncology, School of Medicine, Washington University, St. Louis, MO, USA.
authors.forename: Austen
authors.initials: A
authors.lastname: Curcuru
authors.affiliation: Department of Radiation Oncology, School of Medicine, Washington University, St. Louis, MO, USA.
authors.forename: Olga L
authors.initials: OL
authors.lastname: Green
authors.affiliation: Department of Radiation Oncology, School of Medicine, Washington University, St. Louis, MO, USA.
authors.forename: Baozhou
authors.initials: B
authors.lastname: Sun
authors.affiliation: Department of Radiation Oncology, School of Medicine, Washington University, St. Louis, MO, USA.
authors.forename: Sasa
authors.initials: S
authors.lastname: Mutic
authors.affiliation: Department of Radiation Oncology, School of Medicine, Washington University, St. Louis, MO, USA.
authors.forename: Deshan
authors.initials: D
authors.lastname: Yang
mesh_terms: D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008175:Lung Neoplasms / Q000532:radiotherapy*; D009010:Monte Carlo Method; D010315:Particle Accelerators; D019047:Phantoms, Imaging; D011785:Quality Assurance, Health Care / Q000592:standards*; D011786:Quality Control; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods*; D012984:Software
keywords: IMRT; VMAT; quality assurance; radiation therapy