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Simulation of Ultrasound Images for Validation of MR to Ultrasound Registration in Neurosurgery

  • Hassan Rivaz
  • D. Louis Collins
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8678)

Abstract

Image registration is an essential step in creating augmented environments and performing image-guided interventions. Registration algorithms are commonly validated against simulation and real data. Both validations are critical in a comprehensive analysis: On one hand, the simulation data provides ground truth registration results and can therefore accurately measure the performance of algorithms. It is also flexible and can include different levels of noise and outlier data. On the other hand, real data include factors that are not modeled in simulations and is therefore used to test algorithms against real-world applications. Simulated MR images are provided in the BrainWeb database and have been extensively used to validate and improve image registration algorithms. Simulated US images that correspond to these MR images are of great interest due to the growing interest in the use of ultrasound (US) as a real-time modality that can be easily used during interventions. In this work, we first generate digital brain phantoms by distribution of US scatterers based on the tissue probability maps provided in BrainWeb. We then generate US images of these digital phantoms using the publicly available Field II program. We show that these images look similar to the real US images of the brain. Furthermore, since both the US and MR images are simulated from the same tissue probability map, they are perfectly registered. We then deform the digital phantoms to simulate brain deformations that happen during neurosurgery, and generate US images of the deformed phantoms. We provide some examples for the use of such simulated US images for testing and enhancing image registration algorithms.

Keywords

Image Registration Registration Algorithm Piezoelectric Element Resection Cavity Brain Shift 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Hassan Rivaz
    • 1
  • D. Louis Collins
    • 1
  1. 1.McConnell Brain Imaging CenterMcGill UniversityMontrealCanada

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