• DocumentCode
    2631120
  • Title

    Automatic detection of head refixation errors in fractionated stereotactic radiotherapy (FSR)

  • Author

    Li, S. ; Geng, J. ; Liu, D. ; Rigamonti, D. ; Kleinberg, L. ; He, S. ; DeWeese, T.

  • Author_Institution
    Radiat. Oncology, Johns Hopkins Univ., Baltimore, MD, USA
  • fYear
    2004
  • fDate
    15-18 April 2004
  • Firstpage
    284
  • Abstract
    Patient surface images are acquired using a novel 3D camera when the patient is at the CT-simulation position and after setup for fractionated stereotactic treatment. The simulation and treatment images are aligned through an initial registration using several feature points followed by a refined automatic matching process using an iterative-closest-point mapping-align algorithm. All of the video-surface images could be automatically transformed to the machine coordinate according to the calibration file obtained from a template image. Phantom tests have demonstrated that we can capture surface images of patients in a second with spatial resolution of submillimeter. A millimeter shift and one-degree rotation relative to the treatment machine can be accurately detected. The entire process takes about two minutes. Our primary result on patients involved in a clinical trial is very promising. This research is partially supported by INH Grant 1R43CA91690-01 and NIH CA88843.
  • Keywords
    computerised tomography; image matching; image registration; iterative methods; medical image processing; phantoms; radiation therapy; CT-simulation position; fractionated stereotactic radiotherapy; head refixation errors; image registration; iterative-closest-point mapping-align algorithm; patient surface images; phantom; refined automatic matching process; video-surface images; Calibration; Cameras; Fractionation; Imaging phantoms; Iterative algorithms; Magnetic heads; Medical treatment; Spatial resolution; Surface treatment; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biomedical Imaging: Nano to Macro, 2004. IEEE International Symposium on
  • Print_ISBN
    0-7803-8388-5
  • Type

    conf

  • DOI
    10.1109/ISBI.2004.1398530
  • Filename
    1398530