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Publication Detail
Modelling prostate motion for data fusion during image-guided interventions.
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Journal Article
  • Authors:
    Hu Y, Carter TJ, Ahmed HU, Emberton M, Allen C, Hawkes DJ, Barratt DC
  • Publication date:
  • Pagination:
    1887, 1900
  • Journal:
    IEEE Trans Med Imaging
  • Volume:
  • Issue:
  • Status:
  • Country:
    United States
  • Language:
  • Keywords:
    Computer Simulation, Finite Element Analysis, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Imaging, Three-Dimensional, Male, Models, Statistical, Motion, Prostate, Prostatic Neoplasms, Sensitivity and Specificity, Subtraction Technique, Ultrasonography, Interventional, Ultrasound, High-Intensity Focused, Transrectal
There is growing clinical demand for image registration techniques that allow multimodal data fusion for accurate targeting of needle biopsy and ablative prostate cancer treatments. However, during procedures where transrectal ultrasound (TRUS) guidance is used, substantial gland deformation can occur due to TRUS probe pressure. In this paper, the ability of a statistical shape/motion model, trained using finite element simulations, to predict and compensate for this source of motion is investigated. Three-dimensional ultrasound images acquired on five patient prostates, before and after TRUS-probe-induced deformation, were registered using a nonrigid, surface-based method, and the accuracy of different deformation models compared. Registration using a statistical motion model was found to outperform alternative elastic deformation methods in terms of accuracy and robustness, and required substantially fewer target surface points to achieve a successful registration. The mean final target registration error (based on anatomical landmarks) using this method was 1.8 mm. We conclude that a statistical model of prostate deformation provides an accurate, rapid and robust means of predicting prostate deformation from sparse surface data, and is therefore well-suited to a number of interventional applications where there is a need for deformation compensation.
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