Phantom study and accuracy evaluation of an image-to-world registration approach used with electro-magnetic tracking system for neurosurgery

Senhu Li; David Sarment

doi:10.1117/12.2205853

14 December 2015 Phantom study and accuracy evaluation of an image-to-world registration approach used with electro-magnetic tracking system for neurosurgery

Senhu Li, David Sarment

Author Affiliations +

Proceedings Volume 9814, MIPPR 2015: Parallel Processing of Images and Optimization; and Medical Imaging Processing; 98140A (2015) https://doi.org/10.1117/12.2205853
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

Abstract

Minimally invasive neurosurgery needs intraoperative imaging updates and high efficient image guide system to facilitate the procedure. An automatic image guided system utilized with a compact and mobile intraoperative CT imager was introduced in this work. A tracking frame that can be easily attached onto the commercially available skull clamp was designed. With known geometry of fiducial and tracking sensor arranged on this rigid frame that was fabricated through high precision 3D printing, not only was an accurate, fully automatic registration method developed in a simple and less-costly approach, but also it helped in estimating the errors from fiducial localization in image space through image processing, and in patient space through the calibration of tracking frame. Our phantom study shows the fiducial registration error as 0.348±0.028mm, comparing the manual registration error as 1.976±0.778mm. The system in this study provided a robust and accurate image-to-patient registration without interruption of routine surgical workflow and any user interactions involved through the neurosurgery.

Citation Download Citation

Senhu Li and David Sarment "Phantom study and accuracy evaluation of an image-to-world registration approach used with electro-magnetic tracking system for neurosurgery", Proc. SPIE 9814, MIPPR 2015: Parallel Processing of Images and Optimization; and Medical Imaging Processing, 98140A (14 December 2015); https://doi.org/10.1117/12.2205853

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