title: Automatic CT simulation optimization for radiation therapy: A general strategy.
abstract: OBJECTIVE
  In radiation therapy, x-ray computed tomography (CT) simulation protocol specifications should be driven by the treatment planning requirements in lieu of duplicating diagnostic CT screening protocols. The purpose of this study was to develop a general strategy that allows for automatically, prospectively, and objectively determining the optimal patient-specific CT simulation protocols based on radiation-therapy goals, namely, maintenance of contouring quality and integrity while minimizing patient CT simulation dose.
  
  
  METHODS
  The authors proposed a general prediction strategy that provides automatic optimal CT simulation protocol selection as a function of patient size and treatment planning task. The optimal protocol is the one that delivers the minimum dose required to provide a CT simulation scan that yields accurate contours. Accurate treatment plans depend on accurate contours in order to conform the dose to actual tumor and normal organ positions. An image quality index, defined to characterize how simulation scan quality affects contour delineation, was developed and used to benchmark the contouring accuracy and treatment plan quality within the predication strategy. A clinical workflow was developed to select the optimal CT simulation protocols incorporating patient size, target delineation, and radiation dose efficiency. An experimental study using an anthropomorphic pelvis phantom with added-bolus layers was used to demonstrate how the proposed prediction strategy could be implemented and how the optimal CT simulation protocols could be selected for prostate cancer patients based on patient size and treatment planning task. Clinical IMRT prostate treatment plans for seven CT scans with varied image quality indices were separately optimized and compared to verify the trace of target and organ dosimetry coverage.
  
  
  RESULTS
  Based on the phantom study, the optimal image quality index for accurate manual prostate contouring was 4.4. The optimal tube potentials for patient sizes of 38, 43, 48, 53, and 58 cm were 120, 140, 140, 140, and 140 kVp, respectively, and the corresponding minimum CTDIvol for achieving the optimal image quality index 4.4 were 9.8, 32.2, 100.9, 241.4, and 274.1 mGy, respectively. For patients with lateral sizes of 43-58 cm, 120-kVp scan protocols yielded up to 165% greater radiation dose relative to 140-kVp protocols, and 140-kVp protocols always yielded a greater image quality index compared to the same dose-level 120-kVp protocols. The trace of target and organ dosimetry coverage and the γ passing rates of seven IMRT dose distribution pairs indicated the feasibility of the proposed image quality index for the predication strategy.
  
  
  CONCLUSIONS
  A general strategy to predict the optimal CT simulation protocols in a flexible and quantitative way was developed that takes into account patient size, treatment planning task, and radiation dose. The experimental study indicated that the optimal CT simulation protocol and the corresponding radiation dose varied significantly for different patient sizes, contouring accuracy, and radiation treatment planning tasks.
authors.affiliation: Department of Radiation Oncology, Washington University, St. Louis, Missouri 63110.
authors.forename: Hua
authors.initials: H
authors.lastname: Li
authors.affiliation: Department of Radiology, Mayo Clinic, Rochester, Minnesota 55905.
authors.forename: Lifeng
authors.initials: L
authors.lastname: Yu
authors.affiliation: Department of Biomedical Engineering, Washington University, St. Louis, Missouri 63110.
authors.forename: Mark A
authors.initials: MA
authors.lastname: Anastasio
authors.affiliation: Department of Radiation Oncology, Washington University, St. Louis, Missouri 63110.
authors.forename: Hsin-Chen
authors.initials: HC
authors.lastname: Chen
authors.affiliation: Department of Radiation Oncology, Washington University, St. Louis, Missouri 63110.
authors.forename: Jun
authors.initials: J
authors.lastname: Tan
authors.affiliation: Department of Radiation Oncology, Washington University, St. Louis, Missouri 63110.
authors.forename: Hiram
authors.initials: H
authors.lastname: Gay
authors.affiliation: Department of Radiation Oncology, Washington University, St. Louis, Missouri 63110.
authors.forename: Jeff M
authors.initials: JM
authors.lastname: Michalski
authors.affiliation: Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California 90095.
authors.forename: Daniel A
authors.initials: DA
authors.lastname: Low
authors.affiliation: Department of Radiation Oncology, Washington University, St. Louis, Missouri 63110.
authors.forename: Sasa
authors.initials: S
authors.lastname: Mutic
mesh_terms: D000465:Algorithms; D000886:Anthropometry; D001331:Automation; D003198:Computer Simulation; D006801:Humans; D008297:Male; D010388:Pelvis / Q000000981:diagnostic imaging; D019047:Phantoms, Imaging; D011467:Prostate / Q000000981:diagnostic imaging; D011471:Prostatic Neoplasms / Q000532:radiotherapy*; D011786:Quality Control; D011829:Radiation Dosage; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D011874:Radiometry; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods*; D014584:User-Computer Interface