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Registration of 3D Angiographic and X-Ray Images Using Sequential Monte Carlo Sampling

  • Charles Florin
  • James Williams
  • Ali Khamene
  • Nikos Paragios
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3765)

Abstract

Digital subtraction angiography (DSA) reconstructions and 3D Magnetic Resonance Angiography (MRA) are the modalities of choice for diagnosis of vascular diseases. However, when it comes to treatment through an endovascular intervention, only two dimensional lower resolution information such as angiograms or fluoroscopic images are usually available. Overlaying the pre-operative information from high resoluion acquisition onto the images acquired during intervention greatly helps physician in performing the operation. We propose to register pre-operative DSA or MRS with intra-operative images to bring the two data sets into a single coordinate frame. The method uses the vascular structure, which is present and visible from most of DSA, MRA and x-ray angiogram and fluoroscopic images, to determine the registration parameters. A robust multiple hypothesis framework is built to minimize a fitness measure between the 3D volume and the 2D projection. The measure is based on the distance map computed from the vascular segmentation. Particle Filters are used to resample the hypothesis, and direct them toward the feature space’s zones of maximum likelihood. Promising experimental results demonstrate the potentials of the method.

Keywords

Magnetic Resonance Angiography Digital Subtraction Angiography Gradient Descent Particle Filter Portal Image 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Charles Florin
    • 2
  • James Williams
    • 2
  • Ali Khamene
    • 2
  • Nikos Paragios
    • 1
  1. 1.Ecole Nationale des Ponts et ChausseesFrance
  2. 2.Imaging & Visualization DepartmentSiemens Corporate ResearchPrincetonUSA

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