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Fast rigid registration of pre-operative magnetic resonance images to intra-operative ultrasound for neurosurgery based on high confidence gradient orientations

  • Dante De NigrisEmail author
  • D. Louis Collins
  • Tal Arbel
Original Article

Abstract

Purpose   

We present a novel approach for the registration of pre-operative magnetic resonance images to intra-operative ultrasound images for the context of image-guided neurosurgery.

Method   

Our technique relies on the maximization of gradient orientation alignment in a reduced set of high confidence locations of interest and allows for fast, accurate, and robust registration. Performance is compared with multiple state-of-the-art techniques including conventional intensity-based multi-modal registration strategies, as well as other context-specific approaches. All methods were evaluated on fourteen clinical neurosurgical cases with brain tumors, including low-grade and high-grade gliomas, from the publicly available MNI BITE dataset. Registration accuracy of each method is evaluated as the mean distance between homologous landmarks identified by two or three experts. We provide an analysis of the landmarks used and expose some of the limitations in validation brought forward by expert disagreement and uncertainty in identifying corresponding points.

Results   

The proposed approach yields a mean error of 2.57 mm across all cases (the smallest among all evaluated techniques). Additionally, it is the only evaluated technique that resolves all cases with a mean distance of less than 1 mm larger than the theoretical minimal mean distance when using a rigid transformation.

Conclusion   

Finally, our proposed method provides reduced processing times with an average registration time of 0.76 s in a GPU-based implementation, thereby facilitating its integration into the operating room.

Keywords

Neurosurgery Multi-modal registration GPU Ultrasound 

Notes

Acknowledgments

We would like to thank Dr. Laurence Mercier for her work putting together the MNI BITE dataset, and Simon Drouin and Anka Kochanowska for their technical support for rendering Figs. 2 and 3. We acknowledge funding from the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Canadian Institutes of Health Research (CIHR MOP 97820, MOP-74725 & CHRP).

Conflict of Interest

None.

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Copyright information

© CARS 2013

Authors and Affiliations

  • Dante De Nigris
    • 1
    Email author
  • D. Louis Collins
    • 2
  • Tal Arbel
    • 1
  1. 1.Centre for Intelligent MachinesMcGill UniversityMontrealCanada
  2. 2.Montreal Neurological Institute, McConnell Brain Imaging CentreMcGill UniversityMontrealCanada

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