title: Use of deformed intensity distributions for on-line modification of image-guided IMRT to account for interfractional anatomic changes.
abstract: OBJECTIVE
  Recent imaging studies have demonstrated that there can be significant changes in anatomy from day to day and over the course of radiotherapy as a result of daily positioning uncertainties and physiologic and clinical factors. There are a number of strategies to minimize such changes, reduce their impact, or correct for them. Measures to date have included improved immobilization of external and internal anatomy or adjustment of positions based on portal or ultrasound images. Perhaps the most accurate way is to use CT image-guided radiotherapy, for which the possibilities range from simple correction of setup based on daily CT images to on-line near real-time intensity modulated radiotherapy (IMRT) replanning. In addition, there are numerous intermediate possibilities. In this paper, we report the development of one such intermediate method that takes into account anatomic changes by deforming the intensity distributions of each beam based on deformations of anatomy as seen in the beam's-eye-view.
  
  
  METHODS
  The intensity distribution deformations are computed based on anatomy deformations discerned from the changes in the current image relative to a reference image (e.g., the pretreatment CT scan). First, a reference IMRT plan is generated based on the reference CT image. A new CT image is acquired using an in-room CT for every fraction. The anatomic structure contours are obtained for the new image. (For this article, these contours were manually drawn. When image guided IMRT methods are implemented, anatomic structure contours on subsequent images will likely be obtained with automatic or semiautomatic means. This could be achieved by, for example, first deforming the original CT image to match today's image, and then using the same deformation transformation to map original contours to today's image.) The reference intensity distributions for each beam are then deformed so that the projected geometric relationship within the beam's-eye-view between the anatomy (both target and normal tissues) extracted from the reference image and the reference intensity distribution is the same as (or as close as possible to) the corresponding relationship between anatomy derived from today's image and the newly deformed intensity distributions. To verify whether the dose distributions calculated using the deformed intensity distributions are acceptable for treatment as compared to the original intensity distributions, the deformed intensities are transformed into leaf sequences, which are then used to compute intensity and dose distributions expected to be delivered. The corresponding dose-volume histograms and dose-volume and dose-response indices are also computed. These data are compared with the corresponding data derived (a) from the original treatment plan applied to the original image, (b) from the original treatment plan applied to today's image, and (c) from a new full-fledged IMRT plan designed based on today's image.
  
  
  RESULTS
  Depending on the degree of anatomic changes, the use of an IMRT plan designed based on the original planning CT for the treatment of the current fraction could lead to significant differences compared to the intended dose distributions. CT-guided setup compared to the setup based on skin marks or bony landmarks may improve dose distributions somewhat. Replanning IMRT based on the current fraction's image yields the best physically deliverable plan (the "gold standard"). For the prostate and head-and-neck examples studied as proof of principle, the results of deforming intensities within each beam based on the anatomy seen in the beam's-eye-view are a good approximation of full-fledged replanning compared with other alternatives.
  
  
  CONCLUSIONS
  Our preliminary results encourage us to believe that deforming intensities taking into account deformation in the anatomy may be a rapid way to produce new treatment plans on-line in near real-time based on daily CT images. The methods we have developed need to be applied to a group of patients for both prostate and head-and-neck cases to confirm the validity of our approach.
authors.affiliation: Department of Radiation Physics, Unit 94, The University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA. rmohan@mdanderson.org
authors.forename: Radhe
authors.initials: R
authors.lastname: Mohan
authors.forename: Xiaodong
authors.initials: X
authors.lastname: Zhang
authors.forename: He
authors.initials: H
authors.lastname: Wang
authors.forename: Yixiu
authors.initials: Y
authors.lastname: Kang
authors.forename: Xiaochun
authors.initials: X
authors.lastname: Wang
authors.forename: Helen
authors.initials: H
authors.lastname: Liu
authors.forename: K Kian
authors.initials: KK
authors.lastname: Ang
authors.forename: Deborah
authors.initials: D
authors.lastname: Kuban
authors.forename: Lei
authors.initials: L
authors.lastname: Dong
mesh_terms: D000465:Algorithms; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy*; D006801:Humans; D008297:Male; D009068:Movement; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy*; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D020266:Radiotherapy, Conformal / Q000379:methods*; D012007:Rectum; D014056:Tomography, X-Ray; D001743:Urinary Bladder