2 December 2014 Accurate three-dimensional virtual reconstruction of surgical field using calibrated trajectories of an image-guided medical robot
Yuanzheng Gong, Danying Hu, Blake Hannaford, Eric J. Seibel
Author Affiliations +
Abstract
Brain tumor margin removal is challenging because diseased tissue is often visually indistinguishable from healthy tissue. Leaving residual tumor leads to decreased survival, and removing normal tissue causes life-long neurological deficits. Thus, a surgical robotics system with a high degree of dexterity, accurate navigation, and highly precise resection is an ideal candidate for image-guided removal of fluorescently labeled brain tumor cells. To image, we developed a scanning fiber endoscope (SFE) which acquires concurrent reflectance and fluorescence wide-field images at a high resolution. This miniature flexible endoscope was affixed to the arm of a RAVEN II surgical robot providing programmable motion with feedback control using stereo-pair surveillance cameras. To verify the accuracy of the three-dimensional (3-D) reconstructed surgical field, a multimodal physical-sized model of debulked brain tumor was used to obtain the 3-D locations of residual tumor for robotic path planning to remove fluorescent cells. Such reconstruction is repeated intraoperatively during margin clean-up so the algorithm efficiency and accuracy are important to the robotically assisted surgery. Experimental results indicate that the time for creating this 3-D surface can be reduced to one-third by using known trajectories of a robot arm, and the error from the reconstructed phantom is within 0.67 mm in average compared to the model design.
© 2014 Society of Photo-Optical Instrumentation Engineers (SPIE) 2329-4302/2014/$25.00 © 2014 SPIE
Yuanzheng Gong, Danying Hu, Blake Hannaford, and Eric J. Seibel "Accurate three-dimensional virtual reconstruction of surgical field using calibrated trajectories of an image-guided medical robot," Journal of Medical Imaging 1(3), 035002 (2 December 2014). https://doi.org/10.1117/1.JMI.1.3.035002
Published: 2 December 2014
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CITATIONS
Cited by 13 scholarly publications and 1 patent.
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KEYWORDS
Tumors

Luminescence

3D modeling

3D image processing

Reconstruction algorithms

3D image reconstruction

Reflectivity

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