Proceedings chapter
English

3D organ motion prediction for MR-guided high intensity focused ultrasound

Presented atToronto, 18-22 september 2011
Published inFichtinger, G., Martel, A. and Peters, T. (Ed.), Medical image computing and computer-assisted intervention MICCAI, p. 623-630
PublisherBerlin; New York : Springer
Collection
  • Lecture Notes in Computer Science; 6893
Publication date2011
Abstract

MR-guided High Intensity Focused Ultrasound is an emerging non-invasive technique capable of depositing sharply localised energy deep within the body, without affecting the surrounding tissues. This, however, implies exact knowledge of the target's position when treating mobile organs. In this paper we present an atlas-based prediction technique that trains an atlas from time-resolved 3D volumes using 4DMRI, capturing the full patient specific motion of the organ. Based on a breathing signal, the respiratory state of the organ is then tracked and used to predict the target's future position. To additionally compensate for the non-periodic slower organ drifts, the static motion atlas is combined with a population-based statistical exhalation drift model. The proposed method is validated on organ motion data of 12 healthy volunteers. Experiments estimating the future position of the entire liver result in an average prediction error of 1.1 mm over time intervals of up to 13 minutes.

Keywords
  • Adolescent
  • Adult
  • Aged
  • Algorithms
  • Databases, Factual
  • Female
  • Humans
  • Image Processing, Computer-Assisted
  • Imaging, Three-Dimensional/methods
  • Liver/pathology
  • Magnetic Resonance Imaging/methods
  • Male
  • Middle Aged
  • Motion
  • Reproducibility of Results
  • Respiration
  • Ultrasonography/methods
Citation (ISO format)
ARNOLD, Patrik et al. 3D organ motion prediction for MR-guided high intensity focused ultrasound. In: Medical image computing and computer-assisted intervention MICCAI. Fichtinger, G., Martel, A. and Peters, T. (Ed.). Toronto. Berlin; New York : Springer, 2011. p. 623–630. (Lecture Notes in Computer Science) doi: 10.1007/978-3-642-23629-7_76
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