title: The use of on-line image verification to estimate the variation in radiation therapy dose delivery.
abstract: OBJECTIVE
  On-line radiotherapy imaging systems provide data that allow us to study the geometric nature of treatment variation. It is more clinically relevant to examine the resultant dosimetric variation. In this work, daily beam position as recorded by the on-line images is used to recalculate the treatment plan to show the effect geometric variation has on dose.
  
  
  METHODS
  Daily 6 MV or 18 MV x-ray portal images were acquired using a fiberoptic on-line imaging system for 12 patients with cancers in the head and neck, thoracic, and pelvic regions. Each daily on-line portal image was aligned with the prescription simulation image using a template of anatomical structures defined on the latter. The outline of the actual block position was then superimposed on the prescription image. Daily block positions were cumulated to give a summary image represented by the block overlap isofrequency distribution. The summary data were used to analyze the amount of genometric variation relative to the prescription boundary on a histogram distribution plot. Treatment plans were recalculated by considering each aligned portal image as an individual beam.
  
  
  RESULTS
  On-Line Image Verification (OLIV) data can differentiate between systematic and random errors in a course of daily radiation therapy. The data emphasize that the type and magnitude of patient set-up errors are unique for individual patients and different clinical situations. Head and neck sites had the least random variation (average 0-100% block overlap isofrequency distribution width = 7 mm) compared to thoracic (average 0-100% block overlap isofrequency distribution width = 12 mm) or pelvic sites (average 0-100% block overlap isofrequency distribution width = 14 mm). When treatment delivery is analyzed case by case, systematic as well as random errors are represented. When the data are pooled by anatomical site, individuality of variations is lost and variation appears random. Recalculated plans demonstrated dosimetric deviations from the original plans. The differences between the two dosimetric distributions were emphasized using a technique of plan subtraction. This allowed quick identification of relative "hot and cold spots" in the recalculated plans. The magnitude and clinical significance of dosimetric variation was unique for each patient.
  
  
  CONCLUSIONS
  OLIV data are used to study geometric uncertainties because of the unique nature for individual patients. Dose recalculation is helpful to illustrate the dosimetric consequences of set-up errors.
authors.affiliation: Radiation Oncology Center, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110.
authors.forename: J M
authors.initials: JM
authors.lastname: Michalski
authors.forename: J W
authors.initials: JW
authors.lastname: Wong
authors.forename: R L
authors.initials: RL
authors.lastname: Gerber
authors.forename: D
authors.initials: D
authors.lastname: Yan
authors.forename: A
authors.initials: A
authors.lastname: Cheng
authors.forename: M V
authors.initials: MV
authors.lastname: Graham
authors.forename: M A
authors.initials: MA
authors.lastname: Renna
authors.forename: P J
authors.initials: PJ
authors.lastname: Sawyer
authors.forename: C A
authors.initials: CA
authors.lastname: Perez
mesh_terms: D006801:Humans; D007091:Image Processing, Computer-Assisted; D009369:Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy*; D011859:Radiography; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted