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Biennial International Conference on Information Processing in Medical Imaging

IPMI 1997: Information Processing in Medical Imaging pp 218–231Cite as

Deformation for image guided interventions using a three component tissue model

Part of the Lecture Notes in Computer Science book series (LNCS,volume 1230)

Abstract

In image guided neurosurgery it is necessary to align preoperative image data with the patient. The rigid body approximation is usually applied, but is often not valid due to tissue deformation. Most non-rigid registration algorithms, such as those used for atlas matching, provide a smooth deformation, which does not model the characteristics of different tissues accurately since, for example, bone will appear to deform. We suggest that a physically based model of tissue could provide a powerful tool for tracking tissue movement. Since the algorithm must ultimately run in real time, we have developed a simplified model of tissue deformation based on a three component system. Regions are labelled as either rigid, deformable or fluid. A novel strategy to avoid folding in the transformation is described. Our model was applied to MRI and CT data from a neurosurgery patient with epilepsy. The test data is limited and the current implementation is in 2D, but initial results are promising.

Keywords

  • Thin Plate Spline
  • Fluid Region
  • Tissue Deformation
  • Normal Pressure Hydrocephalus
  • Rigid Registration

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Author information

Authors and Affiliations

  1. Radiological Sciences, UMDS, Guys Hospital, SE1 9RT, London, UK

    P. J. Edwards, D. L. G. Hill, J. A. Little & D. J. Hawkes

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  1. P. J. Edwards
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  2. D. L. G. Hill
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  3. J. A. Little
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  4. D. J. Hawkes
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    © 1997 Springer-Verlag Berlin Heidelberg

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    Edwards, P.J., Hill, D.L.G., Little, J.A., Hawkes, D.J. (1997). Deformation for image guided interventions using a three component tissue model. In: Duncan, J., Gindi, G. (eds) Information Processing in Medical Imaging. IPMI 1997. Lecture Notes in Computer Science, vol 1230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63046-5_17

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    • DOI: https://doi.org/10.1007/3-540-63046-5_17

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