title: Quality assurance for online adapted treatment plans: benchmarking and delivery monitoring simulation.
abstract: OBJECTIVE
  An important challenge facing online adaptive radiation therapy is the development of feasible and efficient quality assurance (QA). This project aimed to validate the deliverability of online adapted plans and develop a proof-of-concept online delivery monitoring system for online adaptive radiation therapy QA.
  
  
  METHODS
  The first part of this project benchmarked automatically online adapted prostate treatment plans using traditional portal dosimetry IMRT QA. The portal dosimetry QA results of online adapted plans were compared to original (unadapted) plans as well as randomly selected prostate IMRT plans from our clinic. In the second part, an online delivery monitoring system was designed and validated via a simulated treatment with intentional multileaf collimator (MLC) errors. This system was based on inputs from the dynamic machine information (DMI), which continuously reports actual MLC positions and machine monitor units (MUs) at intervals of 50 ms or less during delivery. Based on the DMI, the system performed two levels of monitoring/verification during the delivery: (1) dynamic monitoring of cumulative fluence errors resulting from leaf position deviations and visualization using fluence error maps (FEMs); and (2) verification of MLC positions against the treatment plan for potential errors in MLC motion and data transfer at each control point. Validation of the online delivery monitoring system was performed by introducing intentional systematic MLC errors (ranging from 0.5 to 2 mm) to the DMI files for both leaf banks. These DMI files were analyzed by the proposed system to evaluate the system's performance in quantifying errors and revealing the source of errors, as well as to understand patterns in the FEMs. In addition, FEMs from 210 actual prostate IMRT beams were analyzed using the proposed system to further validate its ability to catch and identify errors, as well as establish error magnitude baselines for prostate IMRT delivery.
  
  
  RESULTS
  Online adapted plans were found to have similar delivery accuracy in comparison to clinical IMRT plans when validated with portal dosimetry IMRT QA. FEMs for the simulated deliveries with intentional MLC errors exhibited distinct patterns for different MLC error magnitudes and directions, indicating that the proposed delivery monitoring system is highly specific in detecting the source of errors. Implementing the proposed QA system for online adapted plans revealed excellent delivery accuracy: over 99% of leaf position differences were within 0.5 mm, and >99% of pixels in the FEMs had fluence errors within 0.5 MU. Patterns present in the FEMs and MLC control point analysis for actual patient cases agreed with the error pattern analysis results, further validating the system's ability to reveal and differentiate MLC deviations. Calculation of the fluence map based on the DMI was performed within 2 ms after receiving each DMI input.
  
  
  CONCLUSIONS
  The proposed online delivery monitoring system requires minimal additional resources and time commitment to the current clinical workflow while still maintaining high sensitivity to leaf position errors and specificity to error types. The presented online delivery monitoring system therefore represents a promising QA system candidate for online adaptive radiation therapy.
authors.affiliation: Department of Radiation Oncology, Duke University Medical Center Durham, North Carolina 27710.
authors.forename: Taoran
authors.initials: T
authors.lastname: Li
authors.affiliation: Department of Radiation Oncology, Duke University Medical Center Durham, North Carolina 27710.
authors.forename: Qiuwen
authors.initials: Q
authors.lastname: Wu
authors.affiliation: Department of Radiation Oncology, Duke University Medical Center Durham, North Carolina 27710.
authors.forename: Yun
authors.initials: Y
authors.lastname: Yang
authors.affiliation: Department of Radiation Oncology, Duke University Medical Center Durham, North Carolina 27710.
authors.forename: Anna
authors.initials: A
authors.lastname: Rodrigues
authors.affiliation: Department of Radiation Oncology, Duke University Medical Center Durham, North Carolina 27710.
authors.forename: Fang-Fang
authors.initials: FF
authors.lastname: Yin
authors.affiliation: Department of Radiation Oncology, Duke University Medical Center Durham, North Carolina 27710.
authors.forename: Q
authors.initials: Q
authors.lastname: Jackie Wu
mesh_terms: D019985:Benchmarking / Q000379:methods*; D003198:Computer Simulation; D003200:Computer User Training; D006801:Humans; D007257:Information Theory; D008297:Male; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods; D011471:Prostatic Neoplasms / Q000532:radiotherapy; D011874:Radiometry; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D050397:Radiotherapy, Intensity-Modulated / Q000379:methods