Fast two dimensional to three dimensional registration of fluoroscopy and CT-scans using Octrees on segmentation maps
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1.
ECE Department, UCSB, Santa Barbara, CA 93106
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2.
Department of Computer Science and Department of Mechanical Engineering, University of California at Santa Barbara, CA 93106-5070
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Received:
01 April 2011
Accepted:
29 June 2018
Published:
01 July 2012
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MSC :
Primary: 00A72, 68U10; Secondary: 65N50.
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We introduce a computationally efficient approach to the generation of Digital Reconstructed Radiographs (DRRs) needed to perform three dimensional to two dimensional medical image registration and apply this algorithm to virtual surgery. The DRR generation process is the bottleneck of any three dimensional to two dimensional registration system, since its computational complexity scales with the number of voxels in the Computed Tomography Data, which can be of the order of tens to hundreds of millions. Our approach originates from the segmentation of the volumetric data into multiple regions, which allows a compact representation via
Octree Data Structures. This, in turn, yields efficient storage and access of the attenuation indexes of the volumetric cells, required in the projection procedure that generates the DRR. A functional based on Mutual Information is then maximized to obtain the alignment of the DRR with the two dimensional X-ray fluoroscopy scans
acquired during the operation. Promising experimental results on real data are presented.
Citation: Luca Bertelli, Frédéric Gibou. Fast two dimensional to three dimensional registration of fluoroscopy and CT-scans using Octrees on segmentation maps[J]. Mathematical Biosciences and Engineering, 2012, 9(3): 527-537. doi: 10.3934/mbe.2012.9.527
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Abstract
We introduce a computationally efficient approach to the generation of Digital Reconstructed Radiographs (DRRs) needed to perform three dimensional to two dimensional medical image registration and apply this algorithm to virtual surgery. The DRR generation process is the bottleneck of any three dimensional to two dimensional registration system, since its computational complexity scales with the number of voxels in the Computed Tomography Data, which can be of the order of tens to hundreds of millions. Our approach originates from the segmentation of the volumetric data into multiple regions, which allows a compact representation via
Octree Data Structures. This, in turn, yields efficient storage and access of the attenuation indexes of the volumetric cells, required in the projection procedure that generates the DRR. A functional based on Mutual Information is then maximized to obtain the alignment of the DRR with the two dimensional X-ray fluoroscopy scans
acquired during the operation. Promising experimental results on real data are presented.
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